Refactor detector and model management

* Add combined motion and object Birdseye mode

Add a motion_objects mode that keeps Birdseye active when motion is detected or a confirmed tracked object is present, including stationary objects.

Wire the mode through configuration, runtime commands, API schemas, documentation, and UI labels. Exclude false-positive trackers and add regression coverage for Birdseye activation and MQTT validation.

* Refactor Birdseye activity types as booleans

Replace combination-specific Birdseye modes with composable boolean activity types for motion, active objects, stationary objects, and continuous display.

Preserve legacy single-mode configuration and MQTT inputs, support canonical comma-separated MQTT combinations, and allow scalar YAML values to be replaced by nested settings through the config API.

* Preserve OpenVINO config translations

Regenerate the configuration translations with the OpenVINO detector schema available so the unrelated production detector labels remain intact.

* Preserve partial Birdseye mode overrides

Allow an empty activity selection with a canonical NONE MQTT state so partial camera and profile overrides can disable inherited flags without failing validation.

Add regression coverage for camera and profile inheritance, document the NONE contract, and keep the generated schema fixture scoped to Birdseye.

* Address Birdseye activity review feedback

Move scalar mode compatibility into the 0.18-1 config migration and reject empty activity selections instead of publishing a NONE state.

Pass activity signals through a frozen dataclass, preserve existing active-object tracker behavior, and require confirmed stationary objects. Revert the generic YAML mutation and cover migration, inheritance, MQTT, and activation regressions.

* Move Birdseye migration to 0.19

Use the 0.19-0 configuration revision for converting scalar Birdseye modes to composable activity flags, and update the migration regression coverage accordingly.

* Remove Birdseye migration test

Drop the dedicated config migration test as requested during review while retaining the 0.19-0 migration implementation.

.cspell/frigate-dictionary.txt

@@ -8,6 +8,7 @@ amdgpu
 analyzeduration
 Annke
 apexcharts
+Aqara
 arange
 argmax
 argmin
@@ -64,6 +65,7 @@ dsize
 dtype
 ECONNRESET
 edgetpu
+Eufy
 facenet
 fastapi
 faststart

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.18.0
+VERSION = 0.19.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

audio-labelmap.txt

@@ -24,7 +24,7 @@ yell
 sigh
 singing
 choir
-sodeling
+yodeling
 chant
 mantra
 child_singing

docker/main/build_ov_model.py

@@ -1,10 +1,14 @@
 """Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
 
 Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
-frontend converts the Object Detection API frozen graph natively; the four TF
-outputs are then repacked into the single [1, 1, 100, 7] DetectionOutput-style
-tensor that Frigate's OpenVINO detector expects, and the input is flipped to
-BGR to match the legacy reverse_input_channels behavior.
+frontend translates the Object Detection API pre and post processors literally,
+producing per-class NonMaxSuppression, NonZero ops and map loops with data
+dependent shapes that the GPU plugin handles very badly. Both are cut out the
+way ssd_v2_support.json used to do it: the preprocessor is an identity at the
+native 300x300 input, and the postprocessor becomes a single fused
+DetectionOutput. The result is the [1, 1, 100, 7] tensor that Frigate's
+OpenVINO detector expects, with the input flipped to BGR to match the legacy
+reverse_input_channels behavior.
 """
 
 import numpy as np
@@ -12,31 +16,91 @@ import openvino as ov
 from openvino import opset8 as ops
 from openvino.preprocess import PrePostProcessor
 
+MODEL_DIR = "/models/ssdlite_mobilenet_v2_coco_2018_05_09"
+OUTPUT_PATH = "/models/ssdlite_mobilenet_v2.xml"
+INPUT_SHAPE = [1, 300, 300, 3]
+
+# faster_rcnn_box_coder divides the deltas by pipeline.config's y/x/height/width
+# scales of 10/10/5/5, which DetectionOutput expresses as per-prior variances.
+BOX_VARIANCES = np.float32([0.1, 0.1, 0.2, 0.2])
+
 model = ov.convert_model(
-    "/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
-    input=[("image_tensor:0", [1, 300, 300, 3])],
+    f"{MODEL_DIR}/frozen_inference_graph.pb",
+    input=[("image_tensor:0", INPUT_SHAPE)],
 )
 
-# rows of (image_id, class_id, score, xmin, ymin, xmax, ymax)
-boxes = model.output("detection_boxes:0").get_node().input_value(0)
-classes = model.output("detection_classes:0").get_node().input_value(0)
-scores = model.output("detection_scores:0").get_node().input_value(0)
+nodes = {op.get_friendly_name(): op for op in model.get_ordered_ops()}
+parameter = model.get_parameters()[0]
 
-# (ymin,xmin,ymax,xmax) -> (xmin,ymin,xmax,ymax)
-boxes = ops.gather(boxes, [1, 0, 3, 2], 2)
-classes = ops.unsqueeze(classes, 2)
-scores = ops.unsqueeze(scores, 2)
-image_id = ops.multiply(scores, np.float32(0.0))
+preprocessor = nodes["Preprocessor/map/TensorArrayStack/TensorArrayGatherV3"]
+box_deltas = nodes["Postprocessor/Reshape_1"].output(0)
+class_scores = nodes["Postprocessor/convert_scores"].output(0)
+anchors_output = nodes["Postprocessor/Reshape"].output(0)
 
-detections = ops.concat([image_id, classes, scores, boxes], 2)
-detections = ops.unsqueeze(detections, 1)
+# The anchors only depend on the static input shape, so fold them into a
+# constant and drop the generator subgraph with the rest of the postprocessor.
+probe = ov.Core().compile_model(
+    ov.Model([anchors_output, preprocessor.output(0)], [parameter], "probe"), "CPU"
+)
+probe_input = np.random.default_rng(0).integers(0, 255, INPUT_SHAPE, dtype=np.uint8)
+anchors, resized = (out.copy() for out in probe([probe_input]).values())
+
+assert np.allclose(resized, probe_input, atol=1e-3), (
+    "preprocessor is not an identity at 300x300, it cannot be bypassed"
+)
+
+image = ops.convert(parameter, "f32")
+
+for consumer in list(preprocessor.output(0).get_target_inputs()):
+    consumer.replace_source_output(image.output(0))
+
+# (ymin, xmin, ymax, xmax) -> (xmin, ymin, xmax, ymax)
+priors = anchors[:, [1, 0, 3, 2]].astype(np.float32).reshape(-1)
+variances = np.tile(BOX_VARIANCES, len(anchors))
+proposals = ops.constant(np.stack([priors, variances])[np.newaxis])
+
+# (ty, tx, th, tw) -> (dx, dy, dw, dh) for the CENTER_SIZE decode
+box_logits = ops.reshape(ops.gather(box_deltas, [1, 0, 3, 2], 1), [1, -1], False)
+class_preds = ops.reshape(class_scores, [1, -1], False)
+
+detections = ops.detection_output(
+    box_logits,
+    class_preds,
+    proposals,
+    {
+        "background_label_id": 0,
+        "top_k": 100,
+        "keep_top_k": [100],
+        "nms_threshold": 0.6,
+        "confidence_threshold": 0.3,
+        "code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
+        "share_location": True,
+        "variance_encoded_in_target": False,
+        "normalized": True,
+        "clip_before_nms": False,
+        "clip_after_nms": True,
+        "decrease_label_id": False,
+    },
+)
 detections.output(0).get_tensor().set_names({"detection_out"})
 
-model = ov.Model([detections], model.get_parameters(), "ssdlite_mobilenet_v2")
+model = ov.Model([detections], [parameter], "ssdlite_mobilenet_v2")
 
 ppp = PrePostProcessor(model)
 ppp.input().tensor().set_layout(ov.Layout("NHWC"))
 ppp.input().preprocess().reverse_channels()
 model = ppp.build()
 
-ov.save_model(model, "/models/ssdlite_mobilenet_v2.xml", compress_to_fp16=True)
+# Fail the build rather than silently ship the dynamically shaped graph again.
+op_types = [op.get_type_name() for op in model.get_ordered_ops()]
+assert op_types.count("DetectionOutput") == 1, "postprocessor was not fused"
+
+for dynamic_op in ("NonMaxSuppression", "NonZero", "Loop", "TensorIterator"):
+    assert dynamic_op not in op_types, f"{dynamic_op} left in the graph"
+
+output_shape = model.outputs[0].get_partial_shape()
+assert output_shape.is_static and list(output_shape) == [1, 1, 100, 7], (
+    f"unexpected detector output shape {output_shape}"
+)
+
+ov.save_model(model, OUTPUT_PATH, compress_to_fp16=True)

docker/main/requirements-wheels.txt

@@ -79,7 +79,5 @@ sherpa-onnx==1.12.*
 faster-whisper==1.1.*
 librosa==0.11.*
 soundfile==0.13.*
-# DeGirum detector
-degirum == 0.16.*
 # Memory profiling
 memray == 1.15.*

docs/data/object_detectors_models.yaml

@@ -7,10 +7,9 @@ edgeTPU:
       download: A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
       ui: Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
       yaml: |-
-        detectors:
-          coral:
-            type: edgetpu
-            device: usb
+        models:
+          - devices:
+              - edgetpu:usb
     - key: yolov9
       label: YOLOv9
       recommended: false
@@ -29,17 +28,14 @@ edgeTPU:
         | **Model Input D Type**                   | `int` (Frigate's default value)                                   |
         | **Object Detection Model Type**          | `yolo-generic`                                                    |
       yaml: |-
-        detectors:
-          coral:
-            type: edgetpu
-            device: usb
-
-        model:
-          model_type: yolo-generic
-          width: 320 # <--- should match the imgsize of the model, typically 320
-          height: 320 # <--- should match the imgsize of the model, typically 320
-          path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
-          labelmap_path: /config/labels-coco17.txt
+        models:
+          - devices:
+              - edgetpu:usb
+            model_type: yolo-generic
+            width: 320 # <--- should match the imgsize of the model, typically 320
+            height: 320 # <--- should match the imgsize of the model, typically 320
+            path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
+            labelmap_path: /config/labels-coco17.txt
 hailo8l:
   title: Hailo-8/Hailo-8L
   models:
@@ -62,32 +58,29 @@ hailo8l:
 
         The detector automatically selects the default model based on your hardware. Optionally, specify a local model path or URL to override.
       yaml: |-
-        detectors:
-          hailo:
-            type: hailo8l
-            device: PCIe
+        models:
+          - devices:
+              - hailo8l:PCIe
+            width: 320
+            height: 320
+            input_tensor: nhwc
+            input_pixel_format: rgb
+            input_dtype: int
+            model_type: yolo-generic
+            labelmap_path: /labelmap/coco-80.txt
 
-        model:
-          width: 320
-          height: 320
-          input_tensor: nhwc
-          input_pixel_format: rgb
-          input_dtype: int
-          model_type: yolo-generic
-          labelmap_path: /labelmap/coco-80.txt
-
-          # The detector automatically selects the default model based on your hardware:
-          # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
-          # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
-          #
-          # Optionally, you can specify a local model path to override the default.
-          # If a local path is provided and the file exists, it will be used instead of downloading.
-          # Example:
-          # path: /config/model_cache/hailo/yolov6n.hef
-          #
-          # You can also override using a custom URL:
-          # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
-          # just make sure to give it the write configuration based on the model
+            # The detector automatically selects the default model based on your hardware:
+            # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
+            # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
+            #
+            # Optionally, you can specify a local model path to override the default.
+            # If a local path is provided and the file exists, it will be used instead of downloading.
+            # Example:
+            # path: /config/model_cache/hailo/yolov6n.hef
+            #
+            # You can also override using a custom URL:
+            # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
+            # just make sure to give it the write configuration based on the model
     - key: ssd
       label: SSD MobileNet v1
       recommended: false
@@ -106,23 +99,20 @@ hailo8l:
 
         Specify the local model path or URL for SSD MobileNet v1.
       yaml: |-
-        detectors:
-          hailo:
-            type: hailo8l
-            device: PCIe
-
-        model:
-          width: 300
-          height: 300
-          input_tensor: nhwc
-          input_pixel_format: rgb
-          model_type: ssd
-          # Specify the local model path (if available) or URL for SSD MobileNet v1.
-          # Example with a local path:
-          # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
-          #
-          # Or override using a custom URL:
-          # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
+        models:
+          - devices:
+              - hailo8l:PCIe
+            width: 300
+            height: 300
+            input_tensor: nhwc
+            input_pixel_format: rgb
+            model_type: ssd
+            # Specify the local model path (if available) or URL for SSD MobileNet v1.
+            # Example with a local path:
+            # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+            #
+            # Or override using a custom URL:
+            # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
 openvino:
   title: OpenVINO
   models:
@@ -166,19 +156,16 @@ openvino:
         | **Model Input D Type**                   | `float`                                                  |
         | **Object Detection Model Type**          | `yolo-generic`                                           |
       yaml: |-
-        detectors:
-          ov:
-            type: openvino
-            device: GPU # or NPU
-
-        model:
-          model_type: yolo-generic
-          width: 320 # <--- should match the imgsize set during model export
-          height: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - openvino:GPU
+            model_type: yolo-generic
+            width: 320 # <--- should match the imgsize set during model export
+            height: 320 # <--- should match the imgsize set during model export
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
     - key: ssd
       label: SSDLite MobileNet v2
       recommended: false
@@ -197,18 +184,15 @@ openvino:
         | **Model Input D Type**                   | `int` (Frigate's default value)            |
         | **Object Detection Model Type**          | `ssd` (Frigate's default value)            |
       yaml: |-
-        detectors:
-          ov:
-            type: openvino
-            device: GPU # Or NPU
-
-        model:
-          width: 300
-          height: 300
-          input_tensor: nhwc
-          input_pixel_format: bgr
-          path: /openvino-model/ssdlite_mobilenet_v2.xml
-          labelmap_path: /openvino-model/coco_91cl_bkgr.txt
+        models:
+          - devices:
+              - openvino:GPU
+            width: 300
+            height: 300
+            input_tensor: nhwc
+            input_pixel_format: bgr
+            path: /openvino-model/ssdlite_mobilenet_v2.xml
+            labelmap_path: /openvino-model/coco_91cl_bkgr.txt
     - key: yolo-legacy
       label: YOLO (v3, v4, v7)
       recommended: false
@@ -235,19 +219,16 @@ openvino:
         | **Model Input D Type**                   | `float`                                                  |
         | **Object Detection Model Type**          | `yolo-generic`                                           |
       yaml: |-
-        detectors:
-          ov:
-            type: openvino
-            device: GPU # or NPU
-
-        model:
-          model_type: yolo-generic
-          width: 320 # <--- should match the imgsize set during model export
-          height: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - openvino:GPU
+            model_type: yolo-generic
+            width: 320 # <--- should match the imgsize set during model export
+            height: 320 # <--- should match the imgsize set during model export
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
     - key: yolonas
       label: YOLO-NAS
       recommended: false
@@ -275,19 +256,16 @@ openvino:
         | **Model Input D Type**                   | `int` (Frigate's default value)                   |
         | **Object Detection Model Type**          | `yolonas`                                         |
       yaml: |-
-        detectors:
-          ov:
-            type: openvino
-            device: GPU
-
-        model:
-          model_type: yolonas
-          width: 320 # <--- should match whatever was set in notebook
-          height: 320 # <--- should match whatever was set in notebook
-          input_tensor: nchw
-          input_pixel_format: bgr
-          path: /config/yolo_nas_s.onnx
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - openvino:GPU
+            model_type: yolonas
+            width: 320 # <--- should match whatever was set in notebook
+            height: 320 # <--- should match whatever was set in notebook
+            input_tensor: nchw
+            input_pixel_format: bgr
+            path: /config/yolo_nas_s.onnx
+            labelmap_path: /labelmap/coco-80.txt
     - key: yolox
       label: YOLOX
       recommended: false
@@ -303,15 +281,12 @@ openvino:
         | **Model Input D Type**                | `int` (Frigate's default value)  |
         | **Object Detection Model Type**       | `yolox`                          |
       yaml: |-
-        detectors:
-          ov:
-            type: openvino
-            device: GPU
-
-        model:
-          model_type: yolox
-          path: /config/model_cache/yolox.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - openvino:GPU
+            model_type: yolox
+            path: /config/model_cache/yolox.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
     - key: rfdetr
       label: RF-DETR
       recommended: false
@@ -345,18 +320,15 @@ openvino:
         | **Model Input D Type**                  | `float`                           |
         | **Object Detection Model Type**         | `rfdetr`                          |
       yaml: |-
-        detectors:
-          ov:
-            type: openvino
-            device: GPU
-
-        model:
-          model_type: rfdetr
-          width: 320
-          height: 320
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/rfdetr.onnx # use the filename you generated above
+        models:
+          - devices:
+              - openvino:GPU
+            model_type: rfdetr
+            width: 320
+            height: 320
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/rfdetr.onnx # use the filename you generated above
     - key: dfine
       label: D-FINE / DEIMv2
       recommended: false
@@ -443,19 +415,16 @@ openvino:
         | **Model Input D Type**                   | `float`                            |
         | **Object Detection Model Type**          | `dfine`                            |
       yaml: |-
-        detectors:
-          ov:
-            type: openvino
-            device: CPU
-
-        model:
-          model_type: dfine
-          width: 640
-          height: 640
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/dfine-s.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - openvino:CPU
+            model_type: dfine
+            width: 640
+            height: 640
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/dfine-s.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
 appleSilicon:
   title: Apple Silicon
   models:
@@ -499,19 +468,16 @@ appleSilicon:
         | **Model Input D Type**                   | `float`                                                  |
         | **Object Detection Model Type**          | `yolo-generic`                                           |
       yaml: |-
-        detectors:
-          apple-silicon:
-            type: zmq
-            endpoint: tcp://host.docker.internal:5555
-
-        model:
-          model_type: yolo-generic
-          width: 320 # <--- should match the imgsize set during model export
-          height: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - zmq:tcp://host.docker.internal:5555
+            model_type: yolo-generic
+            width: 320 # <--- should match the imgsize set during model export
+            height: 320 # <--- should match the imgsize set during model export
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
     - key: yolo-legacy
       label: YOLO (v3, v4, v7)
       recommended: false
@@ -538,19 +504,16 @@ appleSilicon:
         | **Model Input D Type**                   | `float`                                                  |
         | **Object Detection Model Type**          | `yolo-generic`                                           |
       yaml: |-
-        detectors:
-          apple-silicon:
-            type: zmq
-            endpoint: tcp://host.docker.internal:5555
-
-        model:
-          model_type: yolo-generic
-          width: 320 # <--- should match the imgsize set during model export
-          height: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - zmq:tcp://host.docker.internal:5555
+            model_type: yolo-generic
+            width: 320 # <--- should match the imgsize set during model export
+            height: 320 # <--- should match the imgsize set during model export
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
 onnx:
   title: ONNX
   models:
@@ -594,18 +557,16 @@ onnx:
         | **Model Input D Type**                   | `float`                                                  |
         | **Object Detection Model Type**          | `yolo-generic`                                           |
       yaml: |-
-        detectors:
-          onnx:
-            type: onnx
-
-        model:
-          model_type: yolo-generic
-          width: 320 # <--- should match the imgsize set during model export
-          height: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - onnx
+            model_type: yolo-generic
+            width: 320 # <--- should match the imgsize set during model export
+            height: 320 # <--- should match the imgsize set during model export
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
     - key: rfdetr
       label: RF-DETR
       recommended: false
@@ -639,17 +600,15 @@ onnx:
         | **Model Input D Type**                  | `float`                           |
         | **Object Detection Model Type**         | `rfdetr`                          |
       yaml: |-
-        detectors:
-          onnx:
-            type: onnx
-
-        model:
-          model_type: rfdetr
-          width: 320
-          height: 320
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/rfdetr.onnx # use the filename you generated above
+        models:
+          - devices:
+              - onnx
+            model_type: rfdetr
+            width: 320
+            height: 320
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/rfdetr.onnx # use the filename you generated above
     - key: yolonas
       label: YOLO-NAS
       recommended: false
@@ -677,18 +636,16 @@ onnx:
         | **Model Input D Type**                   | `int` (Frigate's default value)                   |
         | **Object Detection Model Type**          | `yolonas`                                         |
       yaml: |-
-        detectors:
-          onnx:
-            type: onnx
-
-        model:
-          model_type: yolonas
-          width: 320 # <--- should match whatever was set in notebook
-          height: 320 # <--- should match whatever was set in notebook
-          input_pixel_format: bgr
-          input_tensor: nchw
-          path: /config/yolo_nas_s.onnx
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - onnx
+            model_type: yolonas
+            width: 320 # <--- should match whatever was set in notebook
+            height: 320 # <--- should match whatever was set in notebook
+            input_pixel_format: bgr
+            input_tensor: nchw
+            path: /config/yolo_nas_s.onnx
+            labelmap_path: /labelmap/coco-80.txt
     - key: yolox
       label: YOLOX
       recommended: false
@@ -707,18 +664,16 @@ onnx:
         | **Model Input D Type**                   | `float_denorm`                                           |
         | **Object Detection Model Type**          | `yolox`                                                  |
       yaml: |-
-        detectors:
-          onnx:
-            type: onnx
-
-        model:
-          model_type: yolox
-          width: 416 # <--- should match the imgsize set during model export
-          height: 416 # <--- should match the imgsize set during model export
-          input_tensor: nchw
-          input_dtype: float_denorm
-          path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - onnx
+            model_type: yolox
+            width: 416 # <--- should match the imgsize set during model export
+            height: 416 # <--- should match the imgsize set during model export
+            input_tensor: nchw
+            input_dtype: float_denorm
+            path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
     - key: dfine
       label: D-FINE / DEIMv2
       recommended: false
@@ -805,18 +760,16 @@ onnx:
         | **Model Input D Type**                   | `float`                                     |
         | **Object Detection Model Type**          | `dfine`                                     |
       yaml: |-
-        detectors:
-          onnx:
-            type: onnx
-
-        model:
-          model_type: dfine
-          width: 640
-          height: 640
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - onnx
+            model_type: dfine
+            width: 640
+            height: 640
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
     - key: yolo-legacy
       label: YOLO (v3, v4, v7)
       recommended: false
@@ -843,18 +796,16 @@ onnx:
         | **Model Input D Type**                   | `float`                                                  |
         | **Object Detection Model Type**          | `yolo-generic`                                           |
       yaml: |-
-        detectors:
-          onnx:
-            type: onnx
-
-        model:
-          model_type: yolo-generic
-          width: 320 # <--- should match the imgsize set during model export
-          height: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
-          input_dtype: float
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - onnx
+            model_type: yolo-generic
+            width: 320 # <--- should match the imgsize set during model export
+            height: 320 # <--- should match the imgsize set during model export
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
 cpu:
   title: CPU
   models:
@@ -870,10 +821,9 @@ cpu:
         | **Detector type** | `cpu` |
         | **Num threads**   | `3`   |
       yaml: |-
-        detectors:
-          cpu1:
-            type: cpu
-            num_threads: 3
+        models:
+          - devices:
+              - cpu:3
 deepstack:
   title: DeepStack / CodeProject.AI
   models:
@@ -889,11 +839,9 @@ deepstack:
         | **API URL**   | `http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection`    |
         | **API Timeout** | `0.1` (seconds)                                                      |
       yaml: |-
-        detectors:
-          deepstack:
-            api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
-            type: deepstack
-            api_timeout: 0.1 # seconds
+        models:
+          - devices:
+              - deepstack:http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
 memryx:
   title: MemryX
   models:
@@ -923,23 +871,20 @@ memryx:
         | **Model Input D Type**                   | `float`                                           |
         | **Object Detection Model Type**          | `yolonas`                                         |
       yaml: |-
-        detectors:
-          memx0:
-            type: memryx
-            device: PCIe:0
-
-        model:
-          model_type: yolonas
-          width: 320 # (Can be set to 640 for higher resolution)
-          height: 320 # (Can be set to 640 for higher resolution)
-          input_tensor: nchw
-          input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # path: /config/yolonas.zip
-          # The .zip file must contain:
-          # ├── yolonas.dfp          (a file ending with .dfp)
-          # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
+        models:
+          - devices:
+              - memryx:PCIe:0
+            model_type: yolonas
+            width: 320 # (Can be set to 640 for higher resolution)
+            height: 320 # (Can be set to 640 for higher resolution)
+            input_tensor: nchw
+            input_dtype: float
+            labelmap_path: /labelmap/coco-80.txt
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            # path: /config/yolonas.zip
+            # The .zip file must contain:
+            # ├── yolonas.dfp          (a file ending with .dfp)
+            # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
     - key: yolov9
       label: YOLOv9
       recommended: false
@@ -960,22 +905,19 @@ memryx:
         | **Model Input D Type**                   | `float`                                           |
         | **Object Detection Model Type**          | `yolo-generic`                                    |
       yaml: |-
-        detectors:
-          memx0:
-            type: memryx
-            device: PCIe:0
-
-        model:
-          model_type: yolo-generic
-          width: 320 # (Can be set to 640 for higher resolution)
-          height: 320 # (Can be set to 640 for higher resolution)
-          input_tensor: nchw
-          input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # path: /config/yolov9.zip
-          # The .zip file must contain:
-          # ├── yolov9.dfp          (a file ending with .dfp)
+        models:
+          - devices:
+              - memryx:PCIe:0
+            model_type: yolo-generic
+            width: 320 # (Can be set to 640 for higher resolution)
+            height: 320 # (Can be set to 640 for higher resolution)
+            input_tensor: nchw
+            input_dtype: float
+            labelmap_path: /labelmap/coco-80.txt
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            # path: /config/yolov9.zip
+            # The .zip file must contain:
+            # ├── yolov9.dfp          (a file ending with .dfp)
     - key: yolox
       label: YOLOX
       recommended: false
@@ -996,22 +938,19 @@ memryx:
         | **Model Input D Type**                   | `float_denorm`          |
         | **Object Detection Model Type**          | `yolox`                 |
       yaml: |-
-        detectors:
-          memx0:
-            type: memryx
-            device: PCIe:0
-
-        model:
-          model_type: yolox
-          width: 640
-          height: 640
-          input_tensor: nchw
-          input_dtype: float_denorm
-          labelmap_path: /labelmap/coco-80.txt
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # path: /config/yolox.zip
-          # The .zip file must contain:
-          # ├── yolox.dfp          (a file ending with .dfp)
+        models:
+          - devices:
+              - memryx:PCIe:0
+            model_type: yolox
+            width: 640
+            height: 640
+            input_tensor: nchw
+            input_dtype: float_denorm
+            labelmap_path: /labelmap/coco-80.txt
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            # path: /config/yolox.zip
+            # The .zip file must contain:
+            # ├── yolox.dfp          (a file ending with .dfp)
     - key: ssd
       label: SSDLite MobileNet v2
       recommended: false
@@ -1032,23 +971,20 @@ memryx:
         | **Model Input D Type**                   | `float`                 |
         | **Object Detection Model Type**          | `ssd`                   |
       yaml: |-
-        detectors:
-          memx0:
-            type: memryx
-            device: PCIe:0
-
-        model:
-          model_type: ssd
-          width: 320
-          height: 320
-          input_tensor: nchw
-          input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # path: /config/ssdlite_mobilenet.zip
-          # The .zip file must contain:
-          # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
-          # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
+        models:
+          - devices:
+              - memryx:PCIe:0
+            model_type: ssd
+            width: 320
+            height: 320
+            input_tensor: nchw
+            input_dtype: float
+            labelmap_path: /labelmap/coco-80.txt
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            # path: /config/ssdlite_mobilenet.zip
+            # The .zip file must contain:
+            # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
+            # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
 tensorrt:
   title: TensorRT
   models:
@@ -1082,18 +1018,15 @@ tensorrt:
         | **Model Input D Type**                   | `int` (Frigate's default value)                              |
         | **Object Detection Model Type**          | `ssd` (Frigate's default value)                              |
       yaml: |-
-        detectors:
-          tensorrt:
-            type: tensorrt
-            device: 0 #This is the default, select the first GPU
-
-        model:
-          path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
-          labelmap_path: /labelmap/coco-80.txt
-          input_tensor: nchw
-          input_pixel_format: rgb
-          width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
-          height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
+        models:
+          - devices:
+              - tensorrt:0
+            path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
+            labelmap_path: /labelmap/coco-80.txt
+            input_tensor: nchw
+            input_pixel_format: rgb
+            width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
+            height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
 synaptics:
   title: Synaptics
   models:
@@ -1115,16 +1048,15 @@ synaptics:
         | **Model Input D Type**                   | `int` (Frigate's default value) |
         | **Object Detection Model Type**          | `ssd` (Frigate's default value) |
       yaml: |-
-        detectors: # required
-          synap_npu: # required
-            type: synaptics # required
-
-        model: # required
-          path: /synaptics/mobilenet.synap # required
-          width: 224 # required
-          height: 224 # required
-          input_tensor: nhwc # default value (optional. If you change the model, it is required)
-          labelmap_path: /labelmap/coco-80.txt # required
+        models:
+          -    # required
+            devices:
+              - synaptics
+            path: /synaptics/mobilenet.synap # required
+            width: 224 # required
+            height: 224 # required
+            input_tensor: nhwc # default value (optional. If you change the model, it is required)
+            labelmap_path: /labelmap/coco-80.txt # required
 rknn:
   title: RKNN
   models:
@@ -1149,21 +1081,23 @@ rknn:
         | **Model Input D Type**                   | `int` (Frigate's default value)                    |
         | **Object Detection Model Type**          | `yolo-generic`                                     |
       yaml: |-
-        model: # required
-          # name of model (will be automatically downloaded) or path to your own .rknn model file
-          # possible values are:
-          # - frigate-fp16-yolov9-t
-          # - frigate-fp16-yolov9-s
-          # - frigate-fp16-yolov9-m
-          # - frigate-fp16-yolov9-c
-          # - frigate-fp16-yolov9-e
-          # your yolo_model.rknn
-          path: frigate-fp16-yolov9-t
-          model_type: yolo-generic
-          width: 320
-          height: 320
-          input_tensor: nhwc
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - rknn
+            # name of model (will be automatically downloaded) or path to your own .rknn model file
+            # possible values are:
+            # - frigate-fp16-yolov9-t
+            # - frigate-fp16-yolov9-s
+            # - frigate-fp16-yolov9-m
+            # - frigate-fp16-yolov9-c
+            # - frigate-fp16-yolov9-e
+            # your yolo_model.rknn
+            path: frigate-fp16-yolov9-t
+            model_type: yolo-generic
+            width: 320
+            height: 320
+            input_tensor: nhwc
+            labelmap_path: /labelmap/coco-80.txt
     - key: yolonas
       label: YOLO-NAS
       recommended: false
@@ -1187,20 +1121,22 @@ rknn:
         | **Model Input D Type**                   | `int` (Frigate's default value)                                         |
         | **Object Detection Model Type**          | `yolonas`                                                               |
       yaml: |-
-        model: # required
-          # name of model (will be automatically downloaded) or path to your own .rknn model file
-          # possible values are:
-          # - deci-fp16-yolonas_s
-          # - deci-fp16-yolonas_m
-          # - deci-fp16-yolonas_l
-          # your yolonas_model.rknn
-          path: deci-fp16-yolonas_s
-          model_type: yolonas
-          width: 320
-          height: 320
-          input_pixel_format: bgr
-          input_tensor: nhwc
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - rknn
+            # name of model (will be automatically downloaded) or path to your own .rknn model file
+            # possible values are:
+            # - deci-fp16-yolonas_s
+            # - deci-fp16-yolonas_m
+            # - deci-fp16-yolonas_l
+            # your yolonas_model.rknn
+            path: deci-fp16-yolonas_s
+            model_type: yolonas
+            width: 320
+            height: 320
+            input_pixel_format: bgr
+            input_tensor: nhwc
+            labelmap_path: /labelmap/coco-80.txt
     - key: yolox
       label: YOLOx
       recommended: false
@@ -1222,20 +1158,22 @@ rknn:
         | **Model Input D Type**                   | `int` (Frigate's default value)                |
         | **Object Detection Model Type**          | `yolox`                                        |
       yaml: |-
-        model: # required
-          # name of model (will be automatically downloaded) or path to your own .rknn model file
-          # possible values are:
-          # - rock-i8-yolox_nano
-          # - rock-i8-yolox_tiny
-          # - rock-fp16-yolox_nano
-          # - rock-fp16-yolox_tiny
-          # your yolox_model.rknn
-          path: rock-i8-yolox_nano
-          model_type: yolox
-          width: 416
-          height: 416
-          input_tensor: nhwc
-          labelmap_path: /labelmap/coco-80.txt
+        models:
+          - devices:
+              - rknn
+            # name of model (will be automatically downloaded) or path to your own .rknn model file
+            # possible values are:
+            # - rock-i8-yolox_nano
+            # - rock-i8-yolox_tiny
+            # - rock-fp16-yolox_nano
+            # - rock-fp16-yolox_tiny
+            # your yolox_model.rknn
+            path: rock-i8-yolox_nano
+            model_type: yolox
+            width: 416
+            height: 416
+            input_tensor: nhwc
+            labelmap_path: /labelmap/coco-80.txt
 axengine:
   title: AXEngine
   models:
@@ -1257,6 +1195,7 @@ axengine:
         | **Model Input D Type**                   | `int`                   |
         | **Object Detection Model Type**          | `yolo-generic`          |
       yaml: |-
+<<<<<<< HEAD
         detectors:
           axengine:
             type: axengine
@@ -1269,6 +1208,17 @@ axengine:
           input_dtype: int
           input_pixel_format: bgr
           labelmap_path: /labelmap/coco-80.txt
+=======
+        models:
+          - devices:
+              - axengine
+            path: frigate-yolov9-tiny
+            model_type: yolo-generic
+            width: 320
+            height: 320
+            input_dtype: int
+            input_pixel_format: bgr
+            labelmap_path: /labelmap/coco-80.txt
 degirumAiServer:
   title: DeGirum AI Server
   models:
@@ -1297,11 +1247,13 @@ degirumAiServer:
         | **Zoo** | `degirum/public` |
         | **Token** | your AI Hub token (optional for the public zoo) |
       yaml: |
-        degirum_detector:
-          type: degirum
-          location: degirum
-          zoo: degirum/public
-          token: dg_example_token
+        models:
+          - devices:
+              - degirum:degirum
+            path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+            width: 300
+            height: 300
+            input_pixel_format: rgb
 degirumLocal:
   title: DeGirum Local
   models:
@@ -1318,11 +1270,13 @@ degirumLocal:
         | **Zoo** | `degirum/public` |
         | **Token** | your AI Hub token (optional for the public zoo) |
       yaml: |
-        degirum_detector:
-          type: degirum
-          location: @local
-          zoo: degirum/public
-          token: dg_example_token
+        models:
+          - devices:
+              - degirum:@local
+            path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+            width: 300
+            height: 300
+            input_pixel_format: rgb
 degirumCloud:
   title: DeGirum AI Hub Cloud
   models:
@@ -1339,8 +1293,11 @@ degirumCloud:
         | **Zoo** | `degirum/public` |
         | **Token** | your AI Hub token (optional for the public zoo) |
       yaml: |
-        degirum_detector:
-          type: degirum
-          location: @cloud
-          zoo: degirum/public
-          token: dg_example_token
+        models:
+          - devices:
+              - degirum:@cloud
+            path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+            width: 300
+            height: 300
+            input_pixel_format: rgb
+>>>>>>> 34363affa (Refactor detector and model management)

docs/docs/configuration/advanced/reference.md

@@ -56,17 +56,6 @@ mqtt:
   # 2 = exactly once
   qos: 0
 
-# Optional: Detectors configuration. Defaults to a single CPU detector
-detectors:
-  # Required: name of the detector
-  detector_name:
-    # Required: type of the detector
-    # Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
-    # Additional detector types can also be plugged in.
-    # Detectors may require additional configuration.
-    # Refer to the Detectors configuration page for more information.
-    type: cpu
-
 # Optional: Database configuration
 database:
   # The path to store the SQLite DB (default: shown below)
@@ -157,44 +146,56 @@ auth:
       - front_door
       - back_yard
 
-# Optional: model modifications
+# Optional: object detection models. Defaults to a single model on a CPU detector.
 # NOTE: The default values are for the EdgeTPU detector.
 # Other detectors will require the model config to be set.
-model:
-  # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
-  path: /edgetpu_model.tflite
-  # Required: path to the labelmap (default: shown below)
-  labelmap_path: /labelmap.txt
-  # Required: Object detection model input width (default: shown below)
-  width: 320
-  # Required: Object detection model input height (default: shown below)
-  height: 320
-  # Required: Object detection model input colorspace
-  # Valid values are rgb, bgr, or yuv. (default: shown below)
-  input_pixel_format: rgb
-  # Required: Object detection model input tensor format
-  # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
-  input_tensor: nhwc
-  # Optional: Data type of the model input tensor
-  # Valid values are float, float_denorm, or int (default: shown below)
-  input_dtype: int
-  # Required: Object detection model architecture, used by detectors that support more
-  # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
-  # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
-  model_type: ssd
-  # Required: Label name modifications. These are merged into the standard labelmap.
-  labelmap:
-    2: vehicle
-  # Optional: Map of object labels to their attribute labels (default: depends on model)
-  attributes_map:
-    person:
-      - amazon
-      - face
-    car:
-      - amazon
-      - fedex
-      - license_plate
-      - ups
+models:
+    # Optional: the camera environment this model is for (default: shown below)
+    # Cameras select a model by setting detect -> scene to a matching value, and
+    # a model with a scene of all is used by any camera that does not set one.
+    # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+  - scene: all
+    # Required: hardware this model runs on, as <detector> or <detector>:<device>
+    # See https://docs.frigate.video/configuration/object_detectors for the
+    # detectors available and the devices each one accepts. All of a model's
+    # devices must use the same detector. Listing the same device more than once
+    # runs additional inference processes on it.
+    devices:
+      - edgetpu:pci:0
+    # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
+    path: /edgetpu_model.tflite
+    # Required: path to the labelmap (default: shown below)
+    labelmap_path: /labelmap.txt
+    # Required: Object detection model input width (default: shown below)
+    width: 320
+    # Required: Object detection model input height (default: shown below)
+    height: 320
+    # Required: Object detection model input colorspace
+    # Valid values are rgb, bgr, or yuv. (default: shown below)
+    input_pixel_format: rgb
+    # Required: Object detection model input tensor format
+    # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
+    input_tensor: nhwc
+    # Optional: Data type of the model input tensor
+    # Valid values are float, float_denorm, or int (default: shown below)
+    input_dtype: int
+    # Required: Object detection model architecture, used by detectors that support more
+    # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
+    # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
+    model_type: ssd
+    # Required: Label name modifications. These are merged into the standard labelmap.
+    labelmap:
+      2: vehicle
+    # Optional: Map of object labels to their attribute labels (default: depends on model)
+    attributes_map:
+      person:
+        - amazon
+        - face
+      car:
+        - amazon
+        - fedex
+        - license_plate
+        - ups
 
 # Optional: Audio Events Configuration
 # NOTE: Can be overridden at the camera level
@@ -251,11 +252,17 @@ birdseye:
   # Optional: Encoding quality of the mpeg1 feed (default: shown below)
   # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
   quality: 8
-  # Optional: Mode of the view. Available options are: objects, motion, and continuous
-  #   objects - cameras are included if they have had a tracked object within the last 30 seconds
-  #   motion - cameras are included if motion was detected in the last 30 seconds
-  #   continuous - all cameras are included always
-  mode: objects
+  # Optional: Activity types that include cameras in Birdseye (default: shown below)
+  # Multiple activity types can be enabled at the same time.
+  mode:
+    # Optional: All cameras are included always (default: shown below)
+    continuous: False
+    # Optional: Cameras are included if motion was detected in the last 30 seconds (default: shown below)
+    motion: False
+    # Optional: Cameras are included if they have had an active tracked object within the last 30 seconds (default: shown below)
+    objects: True
+    # Optional: Cameras are included while they have a stationary tracked object (default: shown below)
+    stationary_objects: False
   # Optional: Threshold for camera activity to stop showing camera (default: shown below)
   inactivity_threshold: 30
   # Optional: Configure the birdseye layout
@@ -306,6 +313,10 @@ detect:
   width: 1280
   # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
   height: 720
+  # Optional: the environment this camera looks at, which picks the model it runs on
+  # (default: the model with a scene of all)
+  # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+  scene: outdoor
   # Optional: desired fps for your camera for the input with the detect role (default: shown below)
   # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
   fps: 5
@@ -981,7 +992,9 @@ cameras:
       # Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
       # By default the cameras are sorted alphabetically.
       order: 0
-      # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
+      # Optional: Whether or not to show the camera on the default All Cameras live dashboard.
+      # The camera is still available everywhere else, including camera groups and settings
+      # (default: shown below)
       dashboard: True
       # Optional: Whether this camera is visible in review (the review page and its camera
       # filter, motion review, and the history view) (default: shown below)

docs/docs/configuration/advanced/system.md

@@ -192,12 +192,14 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and open
 
 ```yaml
 # Optional: model config
-model:
-  path: /path/to/model
-  width: 320
-  height: 320
-  input_tensor: "nhwc"
-  input_pixel_format: "bgr"
+models:
+  - devices:
+      - openvino:GPU
+    path: /path/to/model
+    width: 320
+    height: 320
+    input_tensor: "nhwc"
+    input_pixel_format: "bgr"
 ```
 
 </TabItem>
@@ -214,15 +216,15 @@ If the labelmap is customized then the labels used for alerts will need to be ad
 The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
 
 ```yaml
-model:
-  labelmap:
-    2: vehicle
-    3: vehicle
-    5: vehicle
-    7: vehicle
-    15: animal
-    16: animal
-    17: animal
+models:
+  - labelmap:
+      2: vehicle
+      3: vehicle
+      5: vehicle
+      7: vehicle
+      15: animal
+      16: animal
+      17: animal
 ```
 
 Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
@@ -293,6 +295,10 @@ networking:
 
 This setting is for advanced users. For the majority of use cases it's recommended to change the `ports` section of your Docker compose file or use the Docker `run` `--publish` option instead, e.g. `-p 443:8971`. Changing Frigate's ports may break some integrations.
 
+The internal and external ports must be different port numbers, and Frigate will refuse to start otherwise. Requests arriving on the internal port are treated as authenticated admins, so pointing both at the same port would remove authentication from the external one.
+
+Nginx binds these ports when it starts, so port changes only take effect after Frigate restarts.
+
 :::
 
 ### Customizing the Nginx configuration

docs/docs/configuration/audio_detectors.md

@@ -256,7 +256,7 @@ The only field that is valid at the camera level is `enabled`.
 
 #### Live transcription
 
-The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
+The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing, or toggle it outside of the UI with the [`frigate/<camera_name>/audio_transcription/set`](/integrations/mqtt#frigatecamera_nameaudio_transcriptionset) MQTT topic or the HTTP API. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
 
 Results can be error-prone due to a number of factors, including:
 

docs/docs/configuration/birdseye.md

@@ -18,13 +18,14 @@ Each camera tile in Birdseye is composed from the frames of the stream assigned
 
 ## Birdseye Behavior
 
-### Birdseye Modes
+### Birdseye Activity Types
 
-Birdseye offers different modes to customize which cameras show under which circumstances.
+Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be enabled together.
 
-- **continuous:** All cameras are always included
-- **motion:** Cameras that have detected motion within the last 30 seconds are included
-- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
+- **continuous:** The camera is always included
+- **motion:** The camera is included when motion was detected within the last 30 seconds
+- **objects:** The camera is included when an active object was tracked within the last 30 seconds
+- **stationary_objects:** The camera is included while a stationary object is tracked
 
 ### Custom Birdseye Icon
 
@@ -39,27 +40,30 @@ To include a camera in Birdseye view only for specific circumstances, or exclude
 
 **Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
 
-**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
+**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types or disable Birdseye for a specific camera.
 
-| Field               | Description                                                   |
-| ------------------- | ------------------------------------------------------------- |
-| **Enable Birdseye** | Whether this camera appears in Birdseye view                  |
-| **Tracking mode**   | When to show the camera: `continuous`, `motion`, or `objects` |
+| Field                  | Description                                                |
+| ---------------------- | ---------------------------------------------------------- |
+| **Enable Birdseye**    | Whether this camera appears in Birdseye view               |
+| **Activity types**     | Conditions that determine when to show the camera          |
 
 </TabItem>
 <TabItem value="yaml">
 
-```yaml {8-10,12-14}
+```yaml {8-11,13-15}
 # Include all cameras by default in Birdseye view
 birdseye:
   enabled: True
-  mode: continuous
+  mode:
+    continuous: True
 
 cameras:
   front:
     # Only include the "front" camera in Birdseye view when objects are detected
     birdseye:
-      mode: objects
+      mode:
+        continuous: False
+        objects: True
   back:
     # Exclude the "back" camera from Birdseye view
     birdseye:

docs/docs/configuration/cameras.md

@@ -50,6 +50,31 @@ Connect each stream to get a live preview, an estimated bandwidth figure, and a
 
 Other features, including [hardware acceleration](hardware_acceleration_video.md), [two way talk](/configuration/live#two-way-talk), and audio transcoding, is configured after the camera has been added. For camera model specific quirks, see the [camera specific](camera_specific.md) docs.
 
+## Deleting a camera
+
+Click **Delete Camera** in <NavPath path="Settings > Global configuration > Camera management" />, choose the camera, and confirm. Deleting a camera requires the `admin` role and cannot be undone.
+
+:::warning
+
+Deleting a camera permanently removes its recordings, tracked objects, and configuration. If you only want to stop processing a camera, set its state to **Off** or **Disabled** in <NavPath path="Settings > Global configuration > Camera management" /> instead. See [camera state](/configuration/live#camera-state).
+
+:::
+
+Deleting a camera removes:
+
+- The camera's section of your config file, along with its entries in any [role](authentication.md#user-roles) camera list. A custom role left with no cameras is removed as well.
+- Every database record for the camera: tracked objects, review items, recordings, previews, timeline entries, the saved region grid, and [triggers](semantic_search.md#triggers).
+- Every media file for the camera: recordings, snapshots, thumbnails, and preview clips.
+
+[Exports](/usage/exports) are kept by default, so saved footage survives the deletion of the camera it came from. Turn on **Also delete exports for this camera** in the confirmation step to remove those too.
+
+The camera's processes are stopped and the change takes effect immediately, so no restart is required. If the resulting config cannot be parsed, Frigate restores the previous config and reports an error instead of leaving Frigate in a broken state.
+
+Two things are not cleaned up for you:
+
+- **go2rtc streams.** Frigate makes a best effort to stop a running [go2rtc](go2rtc.md) stream named after the camera, but stream entries in your config file remain and are recreated on the next restart. Remove them in <NavPath path="Settings > System > go2rtc streams" /> or in your config file.
+- **Camera groups.** A deleted camera stays listed in any [camera group](#setting-up-camera-groups) that referenced it. The group skips the missing camera, so this is harmless, but you can edit the group to drop the stale entry.
+
 ## Setting Up Camera Inputs
 
 Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.

docs/docs/configuration/config.md

@@ -172,10 +172,9 @@ mqtt:
 ffmpeg:
   hwaccel_args: preset-rpi-64-h264
 
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 
 record:
   enabled: True
@@ -249,10 +248,9 @@ mqtt:
 ffmpeg:
   hwaccel_args: preset-vaapi
 
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 
 record:
   enabled: True
@@ -329,15 +327,12 @@ mqtt:
 ffmpeg:
   hwaccel_args: preset-vaapi
 
-detectors:
-  ov:
-    type: openvino
-    device: AUTO
-
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
+models:
+  - devices:
+      - openvino:AUTO
+    width: 300
+    height: 300
+    input_tensor: nhwc
   input_pixel_format: bgr
   path: /openvino-model/ssdlite_mobilenet_v2.xml
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt

docs/docs/configuration/custom_classification/object_classification.md

@@ -11,7 +11,7 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 
 :::info
 
-Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -11,7 +11,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
 
 :::info
 
-Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/genai/config.md

@@ -6,6 +6,7 @@ title: Configuring Generative AI
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
 
 ## Configuration
 
@@ -13,6 +14,18 @@ A Generative AI provider can be configured in the global config, which will make
 
 `genai` is a map of named providers. Each key under `genai` is a name you choose, and its value is that provider's settings:
 
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
+   - Click **Add** and enter a **Provider name**. Any name of letters, numbers, hyphens, and underscores is accepted, but it cannot be changed from the UI after the provider is created.
+   - Set **Provider** to the service you are using (e.g., `ollama`)
+   - Set **Base URL**, **API key**, and **Model** as required by that provider
+   - Set **Roles** to the roles this provider should handle.
+
+</TabItem>
+<TabItem value="yaml">
+
 ```yaml
 genai:
   my_provider: # any name you like
@@ -25,6 +38,9 @@ genai:
       - chat
 ```
 
+</TabItem>
+</ConfigTabs>
+
 The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
 
 Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
@@ -43,15 +59,20 @@ Running Generative AI models on CPU is not recommended, as high inference times
 
 ### Recommended Local Models
 
-You must use a vision-capable model with Frigate. The following models are recommended for local deployment:
+You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
 
 | Model      | Notes                                                                                                                                                                |
 | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.5`  | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
-| `qwen3.6`  | Strong situational understanding, similar to qwen3-vl                                                                                                                |
+| `qwen3.6`  | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
 | `gemma4`   | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
 
+The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
+
+| Model                | Notes                                                                                                                                                               |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
+
 :::info
 
 Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger sizes are more capable of complex tasks and understanding of situations, but requires more memory and computational resources. It is recommended to try multiple models and experiment to see which performs best.
@@ -416,3 +437,82 @@ genai:
 
 </TabItem>
 </ConfigTabs>
+
+## FAQ
+
+<FaqItem id="how-do-i-debug-genai-issues" question="How do I debug GenAI issues?">
+
+Frigate's Generative AI features are configured and enabled separately. [Review descriptions and summaries](/configuration/genai/genai_review) live under `review.genai`, and [object descriptions](/configuration/genai/genai_objects) live under `objects.genai`. Configuring a provider on this page does not enable either feature, and enabling one does not enable the other. Decide which of the two is not working, then work through the steps below.
+
+1. Confirm a provider is available and holds the `descriptions` role.
+   - Review descriptions, review summaries, and object descriptions all use the provider that has the `descriptions` role assigned in <NavPath path="Settings > Enrichments > Generative AI > Roles" /> (`genai.<provider>.roles`).
+   - A provider is contacted the first time one of its roles is actually used. A provider holding the `embeddings` role for semantic search is initialized during startup, while a `descriptions` provider is not initialized until the first description is requested, which may be well after boot.
+   - In <NavPath path="Settings > Enrichments > Generative AI" />, use **Refresh models** next to the model field. It queries the provider for its model list and is a quick way to verify that the base URL, API key, and network path between Frigate and your provider are correct.
+
+2. Confirm the feature you expect is actually enabled.
+   - Object descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Objects > GenAI object config > Enable GenAI" /> (`objects.genai.enabled`), either globally or per camera. This is the most common reason custom prompts appear to be ignored while review summaries are still being generated.
+   - Review descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Review > GenAI config > Enable GenAI descriptions" /> (`review.genai.enabled`). Once enabled, alerts are described by default but detections are not, so a detection-only review item will never get a summary unless **Enable GenAI for detections** (`review.genai.detections`) is also on.
+
+3. If object descriptions are never requested, check the filters that skip generation.
+   - <NavPath path="Settings > Global configuration > Objects > GenAI object config > GenAI objects" /> (`objects.genai.objects`) limits generation to specific labels, and **Required zones** (`objects.genai.required_zones`) requires the object to have entered one of those zones. If either is set and does not match, Frigate skips the request silently.
+   - Thumbnails are only collected while an object is moving. Objects that go stationary early contribute fewer frames.
+   - **Use snapshots** (`objects.genai.use_snapshot`) requires snapshots to be enabled for the camera. If the snapshot cannot be read, Frigate logs `Cannot load snapshot for <id>, file not found` and no description is generated.
+   - **Send on end** (`objects.genai.send_triggers.tracked_object_end`) is on by default. If you have turned it off in favor of **Early GenAI trigger** (`objects.genai.send_triggers.after_significant_updates`), descriptions are only requested once that number of updates is reached.
+
+4. Enable debug logs to see exactly what Frigate is doing. Restart Frigate after this change. The next step also requires a restart, so turn both on at the same time to avoid restarting twice.
+
+   ```yaml
+   logger:
+     default: info
+     logs:
+       # highlight-start
+       frigate.genai: debug
+       frigate.data_processing.post.object_descriptions: debug
+       frigate.data_processing.post.review_descriptions: debug
+       # highlight-end
+   ```
+
+5. Save the exact images and prompts that were sent to your provider.
+   - Turn on **Save thumbnails** for the feature you are debugging (`review.genai.debug_save_thumbnails` or `objects.genai.debug_save_thumbnails`). Both features write to `/media/frigate/clips/genai-requests/`, and these files are admin-only.
+   - Review descriptions write `genai-requests/<review_id>/` containing the numbered frames that were sent, plus `prompt.txt` and `response.txt` with the exact prompt and the raw, unparsed model response.
+   - Review summary reports write `genai-requests/<start_ts>-<end_ts>/prompt.txt` and `response.txt`. No images are involved, since a report summarizes existing review descriptions.
+   - Object descriptions write `genai-requests/<event_id>/` containing the numbered thumbnails. The prompt for object descriptions is not written to a file, it is only visible in the debug logs from step 4.
+   - Look at the saved images before blaming the model. If the object is small, blurry, or out of frame, no prompt will fix the result. For object descriptions, consider turning on **Use snapshots** (`objects.genai.use_snapshot`) to send a higher quality image. For review items, consider setting **Review image source** (`review.genai.image_source`) to `recordings` for 480p frames instead of the lower resolution preview frames.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+For review descriptions, navigate to <NavPath path="Settings > Global configuration > Review" /> and set **GenAI config > Save thumbnails** to on.
+
+For object descriptions, navigate to <NavPath path="Settings > Global configuration > Objects" />, expand **GenAI object config**, and set **Save thumbnails** to on.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+review:
+  genai:
+    enabled: true
+    # highlight-next-line
+    debug_save_thumbnails: true
+
+objects:
+  genai:
+    enabled: true
+    # highlight-next-line
+    debug_save_thumbnails: true
+```
+
+</TabItem>
+</ConfigTabs>
+
+6. Verify the prompt is what you think it is.
+   - Object description prompts are the ones you control directly. A camera-level <NavPath path="Settings > Camera configuration > Objects > GenAI object config > Caption prompt" /> (`objects.genai.prompt`) overrides the global one, and an entry in **Object prompts** (`objects.genai.object_prompts`) for a label overrides both for that label. Only `{label}`, `{sub_label}`, and `{camera}` are substituted.
+   - Review description prompts are built by Frigate and request a structured JSON response, so they are not fully replaceable. The parts you control are <NavPath path="Settings > Global configuration > Review > GenAI config > Activity context prompt" /> (`review.genai.activity_context_prompt`) and **Additional concerns** (`review.genai.additional_concerns`). Keep the activity context prompt general, since overly specific rules will sway the model's threat level scoring.
+
+7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
+   - Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
+   - Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under <NavPath path="Settings > Enrichments > Generative AI > Provider options" /> (`genai.<provider>.provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
+   - Check **Review Description Speed** and **Object Description Speed** in <NavPath path="System metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
+
+</FaqItem>

docs/docs/configuration/genai/objects.md

@@ -113,3 +113,7 @@ Many providers also have a public facing chat interface for their models. Downlo
 - OpenAI - [ChatGPT](https://chatgpt.com)
 - Gemini - [Google AI Studio](https://aistudio.google.com)
 - Ollama - [Open WebUI](https://docs.openwebui.com/)
+
+## Troubleshooting
+
+If descriptions are not being generated, or the generated descriptions are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).

docs/docs/configuration/genai/review_summaries.md

@@ -201,3 +201,7 @@ Along with individual review item summaries, Generative AI can also produce a si
 Review reports can be requested via the [API](/integrations/api/generate-review-summary-review-summarize-start-start-ts-end-end-ts-post) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
 
 For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.
+
+## Troubleshooting
+
+If summaries are not being generated, or the generated summaries are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).

docs/docs/configuration/go2rtc.md

@@ -67,4 +67,6 @@ If your stream won't play, has no audio, uses excessive CPU, or otherwise misbeh
 
 ## Homekit Configuration
 
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
+To export camera streams to HomeKit, Frigate must be configured in docker to use `host` networking mode. HomeKit settings are stored in `/config/go2rtc_homekit.yml` rather than in your Frigate config, and are edited through the go2rtc config editor at `http://<frigate_host>:1984/editor.html`. Pairings are saved back to that file automatically.
+
+See the [HomeKit integration docs](/integrations/homekit) for the full setup, including the video and audio requirements HomeKit places on the stream.

docs/docs/configuration/live.md

@@ -334,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
 
 - **stalled**
   - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
 
   - Possible console messages from the player code:
     - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`

docs/docs/configuration/notifications.md

@@ -6,6 +6,7 @@ title: Notifications
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
 
 # Notifications
 
@@ -21,7 +22,7 @@ Push notifications require internet access from the Frigate server to the browse
 
 In order to use notifications the following requirements must be met:
 
-- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
+- Frigate must be accessed via a secure `https` connection while signed in as a Frigate user ([see the authorization docs](/configuration/authentication)).
 - A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
 - In order for notifications to be usable externally, Frigate must be accessible externally.
 - For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features.
@@ -85,7 +86,13 @@ cameras:
 
 ### Registration
 
-Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+Once notifications are enabled, press the `Register This Device` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+
+:::warning
+
+Each registration is attached to the Frigate user account you are signed in as, so you must register over a secure connection to the authenticated port (`8971`). Reverse proxies and tunnels should point at port `8971`.
+
+:::
 
 ## Supported Notifications
 
@@ -104,3 +111,62 @@ Different platforms handle notifications differently, some settings changes may
 ### Android
 
 Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
+
+## Notifications FAQ
+
+<FaqItem id="how-do-i-debug-notifications-issues" question="How do I debug notifications issues?">
+
+Push notifications involve Frigate, your browser, and your browser vendor's push service, so it helps to work from the server outward.
+
+1. Enable debug logs for the push client by adding `frigate.comms.webpush: debug` to your `logger` configuration. Restart Frigate after this change.
+
+   ```yaml
+   logger:
+     default: info
+     logs:
+       # highlight-next-line
+       frigate.comms.webpush: debug
+   ```
+
+   These logs show exactly where a notification stopped, including:
+   - `Email must be provided for push notifications to be sent` means the global `email` field is empty and nothing will ever be sent.
+   - `Sending test notification` and `Sending push notification for <camera>, review ID <id>` mean Frigate handed the message off to the push service.
+   - `Skipping notification for <camera> - in global cooldown period` (or `camera-specific cooldown period`) means your [cooldown](#configuration) values suppressed it.
+   - `Notifications for <camera> are currently suspended` means notifications were suspended from <NavPath path="Settings > Notifications" /> or MQTT.
+   - `Notification endpoint expired for <user>, received 410` means that device's subscription is no longer valid and it must be re-registered.
+   - `Failed to send notification to <user> :: <status>` means the push service rejected the message. A `401` or `403` usually points at a VAPID or `email` problem, and a `5xx` is a problem on the push service's end.
+   - If you see no messages at all when an alert occurs, the notification was never queued. Confirm an actual **alert** was created (notifications are not sent for detections), and that notifications are enabled both globally and for that camera.
+
+2. Verify the basics that most reports come down to:
+   - Frigate must be reached over `https` with a certificate your device trusts. Browsers silently refuse to register a service worker otherwise, and a self-signed certificate that is not installed as trusted on the device will fail.
+   - On iOS, notifications only work when Frigate has been installed to the Home Screen via **Share > Add to Home Screen** and opened from that icon. Safari and Chrome tabs cannot receive web push on iOS.
+   - Each device must be registered individually, and Frigate must be restarted after registering before anything can be sent, including test notifications.
+   - The Frigate server needs outbound internet access to the browser vendor's push service. See [Network Requirements](/frigate/network_requirements#push-notifications).
+
+3. Test from the UI. Use the `Send a test notification` button in <NavPath path="Settings > Notifications" />. If the log shows `Sending test notification` but nothing arrives on the device, the problem is between the push service and your device rather than in Frigate.
+
+4. Check the browser side on the device that is not receiving notifications:
+   - Confirm the site's notification permission is set to **Allow** in your browser or OS settings, and that a focus/do not disturb mode is not hiding them.
+   - In desktop browsers, open Developer Tools > Application > Service Workers and confirm `notifications-worker.js` is registered and activated. Unregistering it and registering the device again will rebuild a broken subscription.
+   - Check the browser console and your reverse proxy logs for failures loading `/notifications-worker.js` or errors on `/api/notifications/register`.
+
+</FaqItem>
+
+<FaqItem id="why-did-notifications-stop-arriving-after-working-for-a-while" question="Why did notifications stop arriving after working for a while?">
+
+Push subscriptions are issued by the browser vendor and can be revoked, most often after a browser update, after clearing site data, or when a device has been offline for an extended period. When this happens the device still appears registered in Frigate, but the push service rejects the message. The debug logs will show `Notification endpoint expired` with a `404` or `410` status.
+
+Unregister and re-register the affected device from <NavPath path="Settings > Notifications" />, then restart Frigate.
+
+</FaqItem>
+
+<FaqItem id="why-am-i-not-getting-notifications-for-one-specific-camera" question="Why am I not getting notifications for one specific camera?">
+
+Work through these in order:
+
+- Notifications are only sent for **alerts**. If the camera is producing detections instead, adjust the camera's `review > alerts > labels` so the objects you care about are classified as alerts.
+- Confirm notifications are enabled for that camera in <NavPath path="Settings > Camera configuration > Notifications" />.
+- Check the camera's `cooldown` value, and remember that the global cooldown applies across all cameras. A busy camera can consume the global cooldown and suppress a quieter one.
+- If [authentication](/configuration/authentication) is enabled with roles, users only receive notifications for the cameras their role grants access to.
+
+</FaqItem>

docs/docs/configuration/object_detectors.md

@@ -24,7 +24,6 @@ Frigate supports multiple different detectors that work on different types of ha
 - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
 - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 
 **AMD**
 
@@ -69,12 +68,66 @@ Frigate supports multiple different detectors that work on different types of ha
 
 :::note
 
-Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
+A single model can not be spread across different detector types (ex: OpenVINO and Coral EdgeTPU can not run the same model at the same time). Configuring more than one model, each on its own detector type, is supported.
 
 This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
 
 :::
 
+### Configuring models and hardware
+
+Object detection is configured with a `models` list. Each entry describes one model and the hardware it runs on:
+
+```yaml
+models:
+  - devices:
+      - openvino:GPU
+    path: /config/model_cache/yolov9-s.onnx
+    model_type: yolo-generic
+    width: 320
+    height: 320
+```
+
+Each entry in `devices` is a detector type, optionally followed by a colon and a device for that detector, such as `edgetpu:pci:0`, `openvino:NPU`, or `tensorrt:0`. The per-detector sections below document the device values each one accepts. Listing several devices runs the model on all of them, and listing the **same** device more than once runs additional inference processes against it, which can improve throughput on hardware that keeps up with more than one stream:
+
+```yaml
+models:
+  - devices:
+      - openvino:GPU
+      - openvino:GPU
+```
+
+Coral EdgeTPU and MemryX accelerators can only be opened by one process, so those devices can not be repeated.
+
+### Running more than one model
+
+Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
+
+```yaml
+models:
+  - scene: outdoor
+    path: plus://your-outdoor-model
+    devices:
+      - edgetpu:pci:0
+  - scene: indoor
+    path: /config/model_cache/indoor.onnx
+    model_type: yolo-generic
+    devices:
+      - openvino:GPU
+
+cameras:
+  driveway:
+    detect:
+      scene: outdoor
+    ...
+  hallway:
+    detect:
+      scene: indoor
+    ...
+```
+
+Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
+
 ### Choosing a model size
 
 Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
@@ -93,11 +146,11 @@ The best detection accuracy comes from a model trained on images that look like
 
 # Officially Supported Detectors
 
-Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. Each of a model's devices runs in a dedicated process, and they pull from a common queue of detection requests from the cameras assigned to that model.
 
 ## Edge TPU Detector
 
-The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
+The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To use it, prefix a model's device with `edgetpu`.
 
 The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
 
@@ -118,10 +171,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 ```
 
 </TabItem>
@@ -138,13 +190,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: usb:0
-  coral2:
-    type: edgetpu
-    device: usb:1
+models:
+  - devices:
+      - edgetpu:usb:0
+      - edgetpu:usb:1
 ```
 
 </TabItem>
@@ -163,10 +212,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: ""
+models:
+  - devices:
+      - 'edgetpu:'
 ```
 
 </TabItem>
@@ -183,10 +231,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: pci
+models:
+  - devices:
+      - edgetpu:pci
 ```
 
 </TabItem>
@@ -203,13 +250,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: pci:0
-  coral2:
-    type: edgetpu
-    device: pci:1
+models:
+  - devices:
+      - edgetpu:pci:0
+      - edgetpu:pci:1
 ```
 
 </TabItem>
@@ -226,13 +270,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">
 
 ```yaml
-detectors:
-  coral_usb:
-    type: edgetpu
-    device: usb
-  coral_pci:
-    type: edgetpu
-    device: pci
+models:
+  - devices:
+      - edgetpu:usb
+      - edgetpu:pci
 ```
 
 </TabItem>
@@ -274,7 +315,7 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc
 
 ## OpenVINO Detector
 
-The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
+The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To use it, prefix a model's device with `openvino`.
 
 The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.
 
@@ -287,17 +328,22 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
 
 ```yaml
-detectors:
-  ov_0:
-    type: openvino
-    device: GPU # or NPU
-  ov_1:
-    type: openvino
-    device: GPU # or NPU
+models:
+  - devices:
+      - openvino:GPU # or NPU
+      - openvino:GPU # or NPU
 ```
 
 :::
 
+### Intel NPU host requirements {#intel-npu-requirements}
+
+The NPU firmware is loaded by the host kernel and is not part of the Frigate image. Everything else the NPU needs is bundled in the container, so host NPU libraries should never be mounted in.
+
+Frigate bundles a specific version of Intel's [linux-npu-driver](https://github.com/intel/linux-npu-driver/releases), and the host firmware must come from that release or a newer one. Firmware older than the bundled driver may fail with `MAPPED_INFERENCE_VERSION is NOT compatible with the ELF`, where `Expected` is the version the firmware supports and `received` is the version the bundled compiler produced. Distributions often package older firmware than the driver Frigate ships, so check the build date on the host with `sudo dmesg | grep -i vpu` and update it there if needed.
+
+Intel NPUs cannot be used under Home Assistant OS, which does not include the NPU firmware.
+
 ### Configuration {#configuration-openvino}
 
 <ModelConfigDropdown detectorTitle="OpenVINO" models={objectDetectorsModels.openvino.models} />
@@ -306,6 +352,12 @@ detectors:
 
 ## Apple Silicon detector
 
+:::warning
+
+The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
 The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
 
 ### Setup {#setup-apple-silicon}
@@ -446,11 +498,10 @@ If the correct build is used for your GPU then the GPU will be detected and used
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
 
 ```yaml
-detectors:
-  onnx_0:
-    type: onnx
-  onnx_1:
-    type: onnx
+models:
+  - devices:
+      - onnx
+      - onnx
 ```
 
 :::
@@ -463,7 +514,7 @@ detectors:
 
 ## CPU Detector (not recommended)
 
-The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
+The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To use it, set a model's device to `cpu`.
 
 :::danger
 
@@ -473,7 +524,7 @@ The CPU detector is not recommended for general use. If you do not have GPU or E
 
 The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
 
-A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with the model's `path`.
 
 ### Configuration {#configuration-cpu}
 
@@ -483,6 +534,12 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
 
 ## Deepstack / CodeProject.AI Server Detector
 
+:::warning
+
+The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
 The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
 
 ### Setup {#setup-deepstack}
@@ -545,7 +602,7 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
 
 3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
 
-4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
+4. Bind-mount the `.zip` file into the container and specify its path using the model's `path` in your config.
 
 5. Update `labelmap_path` to match your custom model's labels.
 
@@ -675,13 +732,10 @@ If no custom model is provided, the RKNN detector downloads a default model from
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
 
 ```yaml
-detectors:
-  rknn_0:
-    type: rknn
-    num_cores: 0
-  rknn_1:
-    type: rknn
-    num_cores: 0
+models:
+  - devices:
+      - rknn:0
+      - rknn:0
 ```
 
 :::
@@ -755,8 +809,16 @@ Explanation of the parameters:
   - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
 - `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
 
+<<<<<<< HEAD
+=======
 ## DeGirum
 
+:::warning
+
+The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
 DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
 
 ### Configuration {#configuration-degirum}
@@ -780,20 +842,21 @@ Once completed, configure the detector as follows:
 
 <ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
 
-Setting up a model in the `config.yml` is similar to setting up an AI server.
-You can set it to:
+The model is set on the same `models` entry as the DeGirum device. You can set it to:
 
-- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
+- A model listed on the [AI Hub](https://hub.degirum.com)
   - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
 - A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
 - A path to some model.json.
 
 ```yaml
-model:
-  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+models:
+  - devices:
+      - degirum:<location>
+    path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
+    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
 #### Local Inference
@@ -806,14 +869,16 @@ It is also possible to eliminate the need for an AI server and run the hardware
 
 <ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
 
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
+Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
 
 ```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+models:
+  - devices:
+      - degirum:<location>
+    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
+    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
 #### AI Hub Cloud Inference
@@ -826,16 +891,19 @@ If you do not possess whatever hardware you want to run, there's also the option
 
 <ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
 
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
+Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
 
 ```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+models:
+  - devices:
+      - degirum:<location>
+    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
+    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
+>>>>>>> 34363affa (Refactor detector and model management)
 ## AXERA
 
 Hardware accelerated object detection is supported on the following SoCs:

docs/docs/configuration/profiles.md

@@ -126,7 +126,7 @@ Only the fields you explicitly set in a profile override are applied. All other
 
 ## Activating Profiles
 
-Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), or the Home Assistant integration.
+Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), the [HTTP API](../integrations/api/camera-set-camera-camera-name-set-feature-sub-command-put.api.mdx), or the Home Assistant integration.
 
 In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
 

docs/docs/configuration/review.md

@@ -121,6 +121,31 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
+## Categorizing manual events
+
+Events created with the [create manual event API](../integrations/api/create-event-events-camera-name-label-create-post.api.mdx) are categorized with the same label lists, using the label from the request path:
+
+1. If alerts are enabled and the label is listed in `review -> alerts -> labels`, the review item is an alert.
+2. Otherwise, if detections are enabled and the label is listed in `review -> detections -> labels`, the review item is a detection.
+3. If the label is in neither list, the review item is an alert, or no review item is created if alerts are disabled.
+
+This means manual events are alerts unless you explicitly list their label as a detection label. For example, to have PIR sensors create detections instead of alerts, post to `/api/events/front_door/pir_sensor/create` with the following config:
+
+```yaml {5-7}
+cameras:
+  front_door:
+    review:
+      detections:
+        labels:
+          - pir_sensor
+```
+
+:::note
+
+Required zones do not apply to manual events, since they are created through the API rather than by the object tracker. Setting `review -> alerts -> labels` to an empty list also does not stop manual events from becoming alerts, as a label in neither list still falls back to an alert.
+
+:::
+
 ## Restricting review items to specific zones
 
 By default a review item will be created if any `review -> alerts -> labels` and `review -> detections -> labels` are detected anywhere in the camera frame. You will likely want to configure review items to only be created when the object enters an area of interest, [see the zone docs for more information](./zones.md#restricting-alerts-and-detections-to-specific-zones)

docs/docs/frigate/network_requirements.md

@@ -34,6 +34,12 @@ The following models are downloaded automatically the first time their associate
 | [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
 | [Audio transcription](/configuration/advanced/system)                                         | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
 
+:::note
+
+The MobileNetV2 base weights are the one exception to the `/config/model_cache/` rule. They are also the only entry that is not downloaded when the feature is enabled: Frigate fetches them when a training run actually starts.
+
+:::
+
 ### Hardware-Specific Detector Models
 
 If you are using one of the following hardware detectors and have not provided your own model file, a default model will be downloaded on first startup:
@@ -75,7 +81,7 @@ If your Frigate instance has restricted internet access, you can point model dow
 | `HF_ENDPOINT`                       | `https://huggingface.co`            | Semantic search, Sherpa-ONNX, AXEngine models |
 | `GITHUB_ENDPOINT`                   | `https://github.com`                | Face recognition, LPR, RKNN models            |
 | `GITHUB_RAW_ENDPOINT`               | `https://raw.githubusercontent.com` | Bird classification                           |
-| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Google storage (Keras default)      | Custom classification training                |
+| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Unset (Keras uses its own default)  | Custom classification training                |
 
 ## Optional Cloud Services
 
@@ -147,9 +153,23 @@ When running as a Home Assistant App, the go2rtc startup script queries the loca
 To run Frigate in an air-gapped or offline environment:
 
 1. **Pre-download models**: Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
-2. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
-3. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
-4. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
-5. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
+2. **Pre-download the training base weights**: If you plan to train custom classification models, set `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` before training, then run one training job while online. Without this variable the base weights are cached outside `/config/` and are lost whenever the container is recreated, so a later training run will fail offline. If the machine never has internet access, copy the weights in manually as described below.
+3. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
+4. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
+5. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
+6. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, `GITHUB_RAW_ENDPOINT`, and `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` environment variables to point to local mirrors.
 
 After these steps, Frigate will operate with no outbound internet connections.
+
+### Manually Copying the Training Base Weights
+
+On a machine with internet access, download the weights:
+
+```bash
+curl -L -o mobilenet_v2_weights.h5 \
+  "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_224_no_top.h5"
+```
+
+Copy the file into your Frigate config volume as `/config/model_cache/MobileNet/mobilenet_v2_weights.h5`, keeping that exact filename, then set the environment variable `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` in your Docker compose file to the URL above and restart Frigate.
+
+The variable must be set even though the URL is never contacted. If it is unset, Frigate ignores the copied file and asks Keras to download the weights instead.

docs/docs/guides/getting_started.md

@@ -222,15 +222,12 @@ You need to refer to **Configure hardware acceleration** above to enable the con
 ```yaml {3-6,9-15,20-21}
 mqtt: ...
 
-detectors: # <---- add detectors
-  ov:
-    type: openvino  # <---- use openvino detector
-    device: GPU
-
-# We will use the default MobileNet_v2 model from OpenVINO.
-model:
-  width: 300
-  height: 300
+models: # <---- add models
+  - devices:
+      - openvino:GPU # <---- use the openvino detector on the GPU
+    # We will use the default MobileNet_v2 model from OpenVINO.
+    width: 300
+    height: 300
   input_tensor: nhwc
   input_pixel_format: bgr
   path: /openvino-model/ssdlite_mobilenet_v2.xml
@@ -281,10 +278,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a
 ```yaml {3-6,11-12}
 mqtt: ...
 
-detectors: # <---- add detectors
-  coral:
-    type: edgetpu
-    device: usb
+models: # <---- add models
+  - devices:
+      - edgetpu:usb
 
 cameras:
   name_of_your_camera:
@@ -321,10 +317,9 @@ If you are using YAML to configure Frigate instead of the UI, your configuration
 mqtt:
   enabled: False
 
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 
 cameras:
   name_of_your_camera:
@@ -357,7 +352,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
 ```yaml {16-17}
 mqtt: ...
 
-detectors: ...
+models: ...
 
 cameras:
   name_of_your_camera:

docs/docs/integrations/homekit.md

@@ -3,35 +3,100 @@ id: homekit
 title: HomeKit
 ---
 
-Frigate cameras can be integrated with Apple HomeKit through go2rtc. This allows you to view your camera streams directly in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
+Frigate cameras can be exported to Apple HomeKit through go2rtc. Each exported camera appears as an accessory in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
 
 ## Overview
 
-HomeKit integration is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server to expose your cameras to HomeKit.
+Exporting cameras is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server, so your camera is published to HomeKit as an accessory in its own right.
 
-## Setup
+:::note
 
-All HomeKit configuration and pairing should be done through the **go2rtc WebUI**.
+This is the opposite of importing a HomeKit camera. go2rtc can also pair with an existing HomeKit camera (Aqara, Eve, Eufy, and similar) and use it as a stream source, which is what the `add` page of the go2rtc WebUI is for. That page discovers HomeKit accessories on your network and will not list your Frigate cameras. It is not used for exporting.
 
-### Accessing the go2rtc WebUI
-
-The go2rtc WebUI is available at:
-
-```
-http://<frigate_host>:1984
-```
-
-Replace `<frigate_host>` with the IP address or hostname of your Frigate server.
-
-### Pairing Cameras
-
-1. Navigate to the go2rtc WebUI at `http://<frigate_host>:1984`
-2. Use the `add` section to add a new camera to HomeKit
-3. Follow the on-screen instructions to generate pairing codes for your cameras
+:::
 
 ## Requirements
 
-- Frigate must be accessible on your local network using host network_mode
-- Your iOS device must be on the same network as Frigate
-- Port 1984 must be accessible for the go2rtc WebUI
-- For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc)
+- Frigate must be running with `network_mode: host` so that HomeKit can discover your cameras over mDNS
+- Your Apple device must be on the same network as Frigate
+- Port 1984 must be accessible so you can reach the go2rtc WebUI
+
+HomeKit also places strict limits on the stream itself. go2rtc passes your stream through without resizing or re-encoding it, so the stream you export must already meet these requirements:
+
+- **Video:** H.264 at 1920x1080, 1280x720, or 320x240
+- **Audio:** Opus, mono, 16 kHz
+
+A camera's full resolution stream usually does not qualify. See [Exporting a compatible stream](#exporting-a-compatible-stream) below.
+
+## Configuration
+
+HomeKit settings are stored in `/config/go2rtc_homekit.yml`. This is a separate file from your Frigate config, because go2rtc needs to write your pairings back to it when you pair a device.
+
+Edit it using the go2rtc config editor, which writes to that file directly:
+
+```
+http://<frigate_host>:1984/editor.html
+```
+
+Replace `<frigate_host>` with the IP address or hostname of your Frigate server. The editor will be empty until you add a HomeKit section, since this file holds only your HomeKit settings and not the rest of your go2rtc config.
+
+:::warning
+
+Do not put the `homekit:` section in the `go2rtc:` section of your Frigate config.
+
+Frigate regenerates that config on every startup, so go2rtc cannot save your pairings to it. Pairing will appear to succeed and then fail after the next restart with `PairVerify with unknown client_id`. If the section exists in both places, your saved pairings are erased on every restart.
+
+:::
+
+Add an entry for each camera you want to export. The key must match the name of a go2rtc stream, and the pin must be 8 digits. This is the number the Home app calls the setup code:
+
+```yaml
+homekit:
+  front_door:
+    name: Front Door
+    pin: "12345678"
+```
+
+If the key does not match a go2rtc stream, go2rtc logs `[homekit] missing stream:` at startup and the camera will not appear in the Home app.
+
+:::note
+
+go2rtc derives each accessory's HomeKit identity from this key, so renaming it later means the camera appears as a new accessory and has to be paired again. Settle on the name before you pair.
+
+:::
+
+Frigate keeps only the `homekit:` section of this file when it starts, so do not store streams or other go2rtc settings in it.
+
+### Exporting a compatible stream
+
+If a camera's stream does not meet the requirements listed above, define a scaled restream in your Frigate config and point HomeKit at that stream instead of the original:
+
+```yaml
+go2rtc:
+  streams:
+    front_door:
+      - rtsp://user:password@192.168.1.50:554/stream
+    front_door_homekit:
+      - "ffmpeg:front_door#video=h264#width=1280#height=720#audio=opus/16000"
+```
+
+```yaml
+# /config/go2rtc_homekit.yml
+homekit:
+  front_door_homekit:
+    name: Front Door
+    pin: "12345678"
+```
+
+Add `#hardware=cuda`, `#hardware=vaapi`, or the appropriate value for your system to transcode using your GPU. Note that NVENC cannot encode H.264 wider than 4096 pixels, so very wide streams must be scaled down as shown above rather than only re-encoded.
+
+## Pairing Cameras
+
+1. Restart Frigate after adding the `homekit:` section
+2. In the Apple Home app, choose **Add Accessory**, then **More options** to enter a code manually
+3. Select your camera and enter the pin you configured as the setup code
+4. Confirm that a `pairings:` list now appears under the camera in `/config/go2rtc_homekit.yml`
+
+Pairings are saved back to that file automatically. If step 4 shows no `pairings:` list, check the Frigate log for `[homekit] can't save`, which means the `homekit:` section is missing from `/config/go2rtc_homekit.yml`.
+
+For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc).

docs/docs/integrations/mqtt.md

@@ -292,7 +292,9 @@ Topic with the currently active profile name. Published value is the profile nam
 
 ### `frigate/notifications/set`
 
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications on and off for all cameras. Expected values are `ON` and `OFF`.
+
+Only available when notifications are enabled in the config. Not persisted across Frigate restarts.
 
 ### `frigate/notifications/state`
 
@@ -308,6 +310,8 @@ Publishes the current health status of each role that is enabled (`audio`, `dete
 - `offline`: Stream is offline and is being restarted
 - `disabled`: Camera is currently turned off (either at runtime via the `enabled/set` topic, or persistently via the configuration file). See [Camera state](/configuration/live#camera-state) for the distinction.
 
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
+
 ### `frigate/<camera_name>/<object_name>`
 
 Publishes the count of objects for the camera for use as a sensor in Home Assistant.
@@ -390,6 +394,18 @@ Topic to turn audio detection for a camera on and off. Expected values are `ON`
 
 Topic with current state of audio detection for a camera. Published values are `ON` and `OFF`.
 
+### `frigate/<camera_name>/audio_transcription/set`
+
+Topic to turn [live audio transcription](/configuration/audio_detectors#live-transcription) for a camera on and off. Expected values are `ON` and `OFF`. Transcribed text is published to `frigate/<camera_name>/audio/transcription`.
+
+`ON` is ignored unless audio transcription is enabled in the config for the camera. Unlike the other camera toggles, this one is not persisted across Frigate restarts.
+
+**NOTE:** Requires audio detection and transcription to be enabled
+
+### `frigate/<camera_name>/audio_transcription/state`
+
+Topic with current state of live audio transcription for a camera. Published values are `ON` and `OFF`.
+
 ### `frigate/<camera_name>/recordings/set`
 
 Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
@@ -539,33 +555,38 @@ Topic with current state of Birdseye for a camera. Published values are `ON` and
 
 ### `frigate/<camera_name>/birdseye_mode/set`
 
-Topic to set Birdseye mode for a camera. Birdseye offers different modes to customize under which circumstances the camera is shown.
+Topic to set the Birdseye activity types for a camera. Send one uppercase activity type or combine multiple types with commas, for example `MOTION,OBJECTS,STATIONARY_OBJECTS`.
 
-_Note: Changing the value from `CONTINUOUS` -> `MOTION | OBJECTS` will take up to 30 seconds for
+_Note: Changing the value from `CONTINUOUS` to non-continuous activity types will take up to 30 seconds for
 the camera to be removed from the view._
 
-| Command      | Description                                                       |
-| ------------ | ----------------------------------------------------------------- |
-| `CONTINUOUS` | Always included                                                   |
-| `MOTION`     | Show when detected motion within the last 30 seconds are included |
-| `OBJECTS`    | Shown if an active object tracked within the last 30 seconds      |
+| Command              | Description                                                      |
+| -------------------- | ---------------------------------------------------------------- |
+| `CONTINUOUS`         | Always included                                                  |
+| `MOTION`             | Shown if motion was detected within the last 30 seconds          |
+| `OBJECTS`            | Shown if an active object was tracked within the last 30 seconds |
+| `STATIONARY_OBJECTS` | Shown while a stationary object is tracked                       |
 
 ### `frigate/<camera_name>/birdseye_mode/state`
 
-Topic with current state of the Birdseye mode for a camera. Published values are `CONTINUOUS`, `MOTION`, `OBJECTS`.
+Topic with the current Birdseye activity types for a camera. Multiple enabled types are published as a comma-separated value in the order `OBJECTS`, `MOTION`, `STATIONARY_OBJECTS`, `CONTINUOUS`.
 
 ### `frigate/<camera_name>/notifications/set`
 
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications for a camera on and off. Expected values are `ON` and `OFF`.
+
+`ON` is ignored unless notifications are enabled in the config for the camera. This is not persisted across Frigate restarts. It is the same control the UI labels **Suspend until restart**.
 
 ### `frigate/<camera_name>/notifications/state`
 
-Topic with current state of notifications. Published values are `ON` and `OFF`.
+Topic with current state of notifications. Published values are `ON` and `OFF`. This is the authoritative topic for whether a camera will notify.
 
 ### `frigate/<camera_name>/notifications/suspend`
 
-Topic to suspend notifications for a certain number of minutes. Expected value is an integer.
+Topic to suspend notifications for a certain number of minutes. Expected value is an integer. Separate from `notifications/set`: it does not change `notifications/state`, and is ignored while notifications are off.
 
 ### `frigate/<camera_name>/notifications/suspended`
 
-Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if notifications are not suspended.
+Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if there is no timed suspension.
+
+`0` does not mean notifications are enabled: `notifications/set` `OFF` clears the timed suspension, so this publishes `0` while `notifications/state` is `OFF`.

docs/docs/integrations/plus.md

@@ -62,10 +62,9 @@ Once you have [requested your first model](../plus/first_model.md) and gotten yo
 You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
 
 ```yaml
-detectors: ...
-
-model:
-  path: plus://<your_model_id>
+models:
+  - devices: ...
+    path: plus://<your_model_id>
 ```
 
 :::note
@@ -79,10 +78,11 @@ Models are downloaded into the `/config/model_cache` folder and only downloaded
 If needed, you can override the labelmap for Frigate+ models. This is not recommended as renaming labels will break the Submit to Frigate+ feature if the labels are not available in Frigate+.
 
 ```yaml
-model:
-  path: plus://<your_model_id>
-  labelmap:
-    3: animal
-    4: animal
-    5: animal
+models:
+  - devices: ...
+    path: plus://<your_model_id>
+    labelmap:
+      3: animal
+      4: animal
+      5: animal
 ```

docs/docs/plus/first_model.md

@@ -36,10 +36,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" />. In the *
 <TabItem value="yaml">
 
 ```yaml
-detectors: ...
-
-model:
-  path: plus://<your_model_id>
+models:
+  - devices: ...
+    path: plus://<your_model_id>
 ```
 
 :::tip

docs/docs/troubleshooting/common_errors.md

@@ -34,11 +34,15 @@ The detect FFmpeg process exited on its own. This message is only the notificati
 
 </FaqItem>
 
-<FaqItem id="non-monotonically-increasing-dts" question="Application provided invalid, non monotonically increasing dts to muxer">
+<FaqItem id="non-monotonically-increasing-dts" question="Non-monotonic DTS / non monotonically increasing dts to muxer / Queue input is backward in time">
 
-An FFmpeg message meaning the camera sent packets with out-of-order timestamps. Because recordings are copied without re-encoding, FFmpeg cannot fix them, and the segment muxer often splits early, producing one-second segments and a cache backlog. The usual cause is a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps.
+These are FFmpeg messages indicating the camera sent packets with out-of-order timestamps, either on the video or the audio stream. Timestamp jitter like this is common with WiFi cameras and restreamed or proxied sources; other causes are a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps. A sustained flood of these messages usually precedes the stream stalling and the watchdog restarting FFmpeg.
 
-See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
+In most cases, the fix is to improve the network, reduce system resource usage, or switch to non-WiFi cameras. In general, WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+
+On the video stream, this can affect recordings: because they are copied without re-encoding, FFmpeg cannot fix the timestamps, and the segment muxer often splits early, producing one-second segments and a cache backlog. See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
+
+On the audio stream, the messages can come from the output's audio encoding. If the audio stream is the problem, it may help to have go2rtc transcode it by adding `#audio=aac` to the camera's go2rtc stream to produce clean timestamps for everything consuming the restream.
 
 </FaqItem>
 

docs/docs/troubleshooting/dummy-camera.md

@@ -39,7 +39,7 @@ The per-clip variation is typically quite low and is mostly an artifact of keyfr
 
 Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
 
-The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame.
+The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame. The Debug Replay camera does not save recordings or snapshots or surface anything in Explore, but it otherwise behaves like a regular camera, including running enrichments such as Face Recognition, LPR, and custom classification.
 
 Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
 

docs/docs/troubleshooting/faqs.md

@@ -65,9 +65,17 @@ This is because Frigate does not run in host mode so localhost points to the Fri
 
 ### How do I know if my camera is offline
 
-A camera being offline can be detected via MQTT or /api/stats, the camera_fps for any offline camera will be 0.
+Frigate publishes a per-role health status to [`frigate/<camera_name>/status/<role>`](/integrations/mqtt#frigatecamera_namestatusrole), where `<role>` is each enabled role on the camera (`detect`, `record`, and `audio`). The published value is one of:
 
-Also, Home Assistant will mark any offline camera as being unavailable when the camera is offline.
+- `online`: Frigate's process for that role is running normally
+- `offline`: the process is down and Frigate is restarting it
+- `disabled`: the camera is turned off, either at runtime or in the configuration file
+
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
+
+Because the status is per role, a camera whose substream is fine but whose recording stream has dropped will report `online` for `detect` and `offline` for `record`. The status is republished whenever it changes.
+
+You can also detect an offline camera through `/api/stats`, where `camera_fps` will be 0.
 
 ### How can I view the Frigate log files without using the Web UI?
 

docs/static/frigate-api.yaml

@@ -693,6 +693,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: 
         camera_set_camera__camera_name__set__feature___sub_command__put
       parameters:
@@ -746,6 +783,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: camera_set_camera__camera_name__set__feature__put
       parameters:
         - name: camera_name
@@ -2872,6 +2946,44 @@ paths:
         - frigateUserAuth: []
       x-required-role: any
       description: '**Access:** Any authenticated user.'
+  /categorized_object_names:
+    get:
+      tags:
+        - App
+      summary: Get known object names by object type
+      description: |-
+        **Access:** Any authenticated user.
+
+        Returns the sub labels and attributes this install can attach,
+            grouped by object type. Unlike /sub_labels, which reflects what has already been
+            detected, this reads the config and model files, so it covers recognized face
+            names, named license plates, custom object classification categories, and the
+            detector attributes of tracked objects.
+      operationId: categorized_object_names_categorized_object_names_get
+      parameters:
+        - name: object_type
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: string
+              - type: 'null'
+            title: Object Type
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateUserAuth: []
+      x-required-role: any
   /audio_labels:
     get:
       tags:
@@ -5019,6 +5131,7 @@ paths:
             NOTES:
             - Creating a manual event does not trigger an update to /events MQTT topic.
             - If a duration is set to null, the event will need to be ended manually by calling /events/{event_id}/end.
+            - The review item is an alert unless the label is listed in the camera's review -> detections -> labels config.
       operationId: create_event_events__camera_name___label__create_post
       parameters:
         - name: camera_name

frigate/api/app.py

@@ -31,7 +31,10 @@ from frigate.api.auth import (
     get_allowed_cameras_for_filter,
     require_role,
 )
-from frigate.api.config_util import swap_runtime_config
+from frigate.api.config_util import (
+    publish_camera_section_updates,
+    swap_runtime_config,
+)
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
 from frigate.api.defs.request.app_body import (
     AppConfigSetBody,
@@ -68,6 +71,7 @@ from frigate.util.config import (
     find_config_file,
     redact_credential,
 )
+from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
     get_nvidia_driver_info,
@@ -288,10 +292,6 @@ def config(request: Request):
     config: dict[str, dict[str, Any]] = config_obj.model_dump(
         mode="json", warnings="none", exclude_none=True
     )
-    config["detectors"] = {
-        name: detector.model_dump(mode="json", warnings="none", exclude_none=True)
-        for name, detector in config_obj.detectors.items()
-    }
 
     # remove environment_vars for non-admin users
     if request.headers.get("remote-role") != "admin":
@@ -372,31 +372,28 @@ def config(request: Request):
         config["go2rtc"]["streams"][stream_name] = cleaned
 
     config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
-    config["model"]["colormap"] = config_obj.model.colormap
-    config["model"]["all_attributes"] = config_obj.model.all_attributes
-    config["model"]["non_logo_attributes"] = config_obj.model.non_logo_attributes
 
-    # Add model plus data if plus is enabled
-    if config["plus"]["enabled"]:
-        model_path = config.get("model", {}).get("path")
-        if model_path:
-            model_json_path = FilePath(model_path).with_suffix(".json")
+    for index, model in enumerate(config_obj.models):
+        model_dict = config["models"][index]
+        model_dict["colormap"] = model.colormap
+        model_dict["all_attributes"] = model.all_attributes
+        model_dict["non_logo_attributes"] = model.non_logo_attributes
+        model_dict["labelmap"] = model.merged_labelmap
+
+        if not config["plus"]["enabled"]:
+            continue
+
+        # Add model plus data if plus is enabled
+        model_dict["plus"] = None
+
+        if model.path:
+            model_json_path = FilePath(model.path).with_suffix(".json")
+
             try:
                 with open(model_json_path) as f:
-                    model_plus_data = json.load(f)
-                config["model"]["plus"] = model_plus_data
-            except FileNotFoundError:
-                config["model"]["plus"] = None
-            except json.JSONDecodeError:
-                config["model"]["plus"] = None
-        else:
-            config["model"]["plus"] = None
-
-    # use merged labelamp
-    for detector_config in config["detectors"].values():
-        detector_config["model"]["labelmap"] = (
-            request.app.frigate_config.model.merged_labelmap
-        )
+                    model_dict["plus"] = json.load(f)
+            except (FileNotFoundError, json.JSONDecodeError):
+                pass
 
     return JSONResponse(content=config)
 
@@ -963,6 +960,17 @@ def config_set(request: Request, body: AppConfigSetBody):
                             body.update_topic, settings
                         )
 
+                        # a config/cameras/* topic publishes camera copies, a
+                        # global topic the global object. FrigateConfig.parse
+                        # folds some global sections down into every camera,
+                        # and workers read both objects, so any such section
+                        # needs its camera copies sent alongside the global
+                        # publish above.
+                        if body.update_topic == "config/birdseye":
+                            publish_camera_section_updates(
+                                request.app, config, CameraConfigUpdateEnum.birdseye
+                            )
+
             return JSONResponse(
                 content=(
                     {
@@ -1299,6 +1307,28 @@ def get_sub_labels(
     return JSONResponse(content=sub_labels)
 
 
+@router.get(
+    "/categorized_object_names",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get known object names by object type",
+    description="""Returns the sub labels and attributes this install can attach,
+    grouped by object type. Unlike /sub_labels, which reflects what has already been
+    detected, this reads the config and model files, so it covers recognized face
+    names, named license plates, custom object classification categories, and the
+    detector attributes of tracked objects.""",
+)
+def categorized_object_names(
+    request: Request,
+    object_type: str | None = None,
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+):
+    return JSONResponse(
+        content=get_categorized_object_names(
+            request.app.frigate_config, allowed_cameras, object_type
+        )
+    )
+
+
 @router.get("/audio_labels", dependencies=[Depends(allow_any_authenticated())])
 def get_audio_labels():
     labels = load_labels("/audio-labelmap.txt", prefill=521)
@@ -1323,11 +1353,14 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
 
     modelList = models["list"]
 
+    config: FrigateConfig = request.app.frigate_config
+    primary_model = config.primary_model
+
     # current model type
-    modelType = request.app.frigate_config.model.model_type
+    modelType = primary_model.model_type
 
     # current detectorType for comparing to supportedDetectors
-    detectorType = list(request.app.frigate_config.detectors.values())[0].type
+    detectorType = config.devices_for_model(primary_model)[0].detector
 
     validModels = []
 

frigate/api/auth.py

@@ -31,7 +31,7 @@ from frigate.api.media_auth import (
     deny_response_for_media_uri,
     is_role_restricted,
 )
-from frigate.config import AuthConfig, NetworkingConfig, ProxyConfig
+from frigate.config import AuthConfig, ProxyConfig
 from frigate.const import CONFIG_DIR, JWT_SECRET_ENV_VAR, PASSWORD_HASH_ALGORITHM
 from frigate.models import User
 
@@ -83,6 +83,7 @@ def require_admin_by_default():
         "/nvinfo",
         "/labels",
         "/sub_labels",
+        "/categorized_object_names",
         "/plus/models",
         "/recognized_license_plates",
         "/timeline",
@@ -620,18 +621,18 @@ def resolve_role(
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
-    networking_config: NetworkingConfig = request.app.frigate_config.networking
 
     success_response = Response("", status_code=202)
 
-    # handle case where internal port is a string with ip:port
-    internal_port = networking_config.listen.internal
-    if type(internal_port) is str:
-        internal_port = int(internal_port.split(":")[-1])
-
     # dont require auth if the request is on the internal port
-    # this header is set by Frigate's nginx proxy, so it cant be spoofed
-    if int(request.headers.get("x-server-port", default=0)) == internal_port:
+    # this header is set by Frigate's nginx proxy, so it cant be spoofed.
+    # the port is the boot-time snapshot rather than the live config value:
+    # nginx's listeners are fixed at container start, so an in-memory config
+    # change must never move the port that is trusted here
+    if (
+        int(request.headers.get("x-server-port", default=0))
+        == request.app.auth_internal_port
+    ):
         success_response.headers["remote-user"] = "anonymous"
         success_response.headers["remote-role"] = "admin"
         return success_response

frigate/api/camera.py

@@ -1328,7 +1328,45 @@ def camera_set(
     body: CameraSetBody,
     sub_command: str | None = None,
 ):
-    """Set a camera feature state. Use camera_name='*' to target all cameras."""
+    """Set a camera feature state. Use camera_name='*' to target all cameras.
+
+    The value to set is sent in the request body as `{"value": "<value>"}`.
+
+    | Feature | Accepted values |
+    | --- | --- |
+    | `enabled` | `ON`, `OFF` |
+    | `detect` | `ON`, `OFF` |
+    | `motion` | `ON`, `OFF` |
+    | `recordings` | `ON`, `OFF` |
+    | `snapshots` | `ON`, `OFF` |
+    | `audio` | `ON`, `OFF` |
+    | `audio_transcription` | `ON`, `OFF` |
+    | `notifications` | `ON`, `OFF` |
+    | `review_alerts` | `ON`, `OFF` |
+    | `review_detections` | `ON`, `OFF` |
+    | `object_descriptions` | `ON`, `OFF` |
+    | `review_descriptions` | `ON`, `OFF` |
+    | `improve_contrast` | `ON`, `OFF` |
+    | `ptz_autotracker` | `ON`, `OFF` |
+    | `birdseye` | `ON`, `OFF` |
+    | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+    | `motion_contour_area` | integer |
+    | `motion_threshold` | integer |
+    | `motion_mask` | `ON`, `OFF` |
+    | `object_mask` | `ON`, `OFF` |
+    | `zone` | `ON`, `OFF` |
+    | `profile` | a profile name, or `none` to deactivate |
+
+    `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+    parameter to be set to the name of the mask or zone. All other features
+    reject a sub-command.
+
+    `profile` applies globally rather than per camera, so it requires
+    `camera_name` to be `*`.
+
+    These features map to the equivalent MQTT topics, which document the
+    behavior of each value in more detail.
+    """
     dispatcher = request.app.dispatcher
     frigate_config: FrigateConfig = request.app.frigate_config
 

frigate/api/chat.py

@@ -50,6 +50,7 @@ from frigate.jobs.vlm_watch import (
     stop_vlm_watch_job,
 )
 from frigate.models import Event
+from frigate.util.object_names import get_categorized_object_names
 
 logger = logging.getLogger(__name__)
 
@@ -539,6 +540,11 @@ async def execute_tool(
     if tool_name == "search_objects":
         return await _execute_search_objects(request, arguments, allowed_cameras)
 
+    if tool_name == "get_categorized_object_names":
+        return JSONResponse(
+            content=_execute_get_categorized_object_names(request, allowed_cameras)
+        )
+
     if tool_name == "find_similar_objects":
         result = await _execute_find_similar_objects(
             request, arguments, allowed_cameras
@@ -717,6 +723,21 @@ async def _execute_set_camera_state(
     return {"success": True, "camera": camera, "feature": feature, "value": value}
 
 
+def _execute_get_categorized_object_names(
+    request: Request,
+    allowed_cameras: list[str],
+) -> dict[str, Any]:
+    names = get_categorized_object_names(request.app.frigate_config, allowed_cameras)
+
+    if not names:
+        return {
+            "names": {},
+            "message": "No names configured; search by label or semantic_query.",
+        }
+
+    return {"names": names}
+
+
 async def _execute_tool_internal(
     tool_name: str,
     arguments: dict[str, Any],
@@ -741,6 +762,8 @@ async def _execute_tool_internal(
         except (json.JSONDecodeError, AttributeError) as e:
             logger.warning(f"Failed to extract tool result: {e}")
             return {"error": "Failed to parse tool result"}
+    elif tool_name == "get_categorized_object_names":
+        return _execute_get_categorized_object_names(request, allowed_cameras)
     elif tool_name == "find_similar_objects":
         return await _execute_find_similar_objects(request, arguments, allowed_cameras)
     elif tool_name == "set_camera_state":
@@ -773,8 +796,8 @@ async def _execute_tool_internal(
     else:
         logger.error(
             "Tool call failed: unknown tool %r. Expected one of: search_objects, find_similar_objects, "
-            "get_live_context, start_camera_watch, stop_camera_watch, get_profile_status, get_recap. "
-            "Arguments received: %s",
+            "get_categorized_object_names, get_live_context, start_camera_watch, stop_camera_watch, "
+            "get_profile_status, get_recap. Arguments received: %s",
             tool_name,
             json.dumps(arguments),
         )

frigate/api/classification.py

@@ -11,7 +11,6 @@ from typing import Any
 import cv2
 from fastapi import APIRouter, Depends, Request, UploadFile
 from fastapi.responses import JSONResponse
-from pathvalidate import sanitize_filename
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
@@ -43,12 +42,21 @@ from frigate.util.classification import (
     write_training_metadata,
 )
 from frigate.util.file import get_event_snapshot
+from frigate.util.path import safe_join, sanitize_path_component
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.classification])
 
 
+def invalid_name_response(value: str) -> JSONResponse:
+    """Response for a name that cannot be used as a path component."""
+    return JSONResponse(
+        content={"success": False, "message": f"Invalid name: {value}"},
+        status_code=400,
+    )
+
+
 @router.get(
     "/faces",
     response_model=FacesResponse,
@@ -98,9 +106,7 @@ def reclassify_face(request: Request, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    training_file = os.path.join(
-        FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
-    )
+    training_file = safe_join(FACE_DIR, "train", json.get("training_file", ""))
 
     if not training_file or not os.path.isfile(training_file):
         return JSONResponse(
@@ -150,8 +156,10 @@ def train_face(request: Request, name: str, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(FACE_DIR, f"train/{training_file_name}")
+    training_file_name = json.get("training_file", "")
+    training_file = (
+        safe_join(FACE_DIR, "train", training_file_name) if training_file_name else None
+    )
     event_id = json.get("event_id")
 
     if not training_file_name and not event_id:
@@ -165,7 +173,9 @@ def train_face(request: Request, name: str, body: dict = None):
             status_code=400,
         )
 
-    if training_file_name and not os.path.isfile(training_file):
+    if training_file_name and (
+        training_file is None or not os.path.isfile(training_file)
+    ):
         return JSONResponse(
             content=(
                 {
@@ -176,9 +186,13 @@ def train_face(request: Request, name: str, body: dict = None):
             status_code=404,
         )
 
-    sanitized_name = sanitize_filename(name)
+    sanitized_name = sanitize_path_component(name)
+    new_file_folder = safe_join(FACE_DIR, name)
+
+    if sanitized_name is None or new_file_folder is None:
+        return invalid_name_response(name)
+
     new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
-    new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
 
     os.makedirs(new_file_folder, exist_ok=True)
 
@@ -261,9 +275,12 @@ async def create_face(request: Request, name: str):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
-    os.makedirs(
-        os.path.join(FACE_DIR, sanitize_filename(name.replace(" ", "_"))), exist_ok=True
-    )
+    face_folder = safe_join(FACE_DIR, name.replace(" ", "_"))
+
+    if face_folder is None:
+        return invalid_name_response(name)
+
+    os.makedirs(face_folder, exist_ok=True)
     return JSONResponse(
         status_code=200,
         content={"success": False, "message": "Successfully created face folder."},
@@ -287,6 +304,9 @@ def register_face(request: Request, name: str, file: UploadFile):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
+    if sanitize_path_component(name) is None:
+        return invalid_name_response(name)
+
     context: EmbeddingsContext = request.app.embeddings
     result = None if context is None else context.register_face(name, file.file.read())
 
@@ -356,8 +376,8 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    image_id = sanitize_filename(json.get("id", ""))
-    new_name = sanitize_filename(json.get("new_name", ""))
+    image_id = sanitize_path_component(json.get("id", ""))
+    new_name = sanitize_path_component(json.get("new_name", ""))
 
     if not image_id or not new_name:
         return JSONResponse(
@@ -381,7 +401,12 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
             status_code=400,
         )
 
-    source_folder = os.path.join(FACE_DIR, sanitize_filename(name))
+    source_folder = safe_join(FACE_DIR, name)
+    target_folder = safe_join(FACE_DIR, new_name)
+
+    if source_folder is None or target_folder is None:
+        return invalid_name_response(name)
+
     source_file = os.path.join(source_folder, image_id)
 
     if not os.path.isfile(source_file):
@@ -396,7 +421,6 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
         )
 
     target_filename = f"{new_name}-{datetime.datetime.now().timestamp()}.webp"
-    target_folder = os.path.join(FACE_DIR, new_name)
 
     os.makedirs(target_folder, exist_ok=True)
     shutil.move(source_file, os.path.join(target_folder, target_filename))
@@ -430,8 +454,19 @@ def deregister_faces(request: Request, name: str, body: DeleteFaceImagesBody):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
+    sanitized_name = sanitize_path_component(name)
+
+    if sanitized_name is None:
+        return invalid_name_response(name)
+
+    sanitized_ids = [
+        component
+        for component in map(sanitize_path_component, body.ids)
+        if component is not None
+    ]
+
     context: EmbeddingsContext = request.app.embeddings
-    context.delete_face_ids(name, map(lambda file: sanitize_filename(file), body.ids))
+    context.delete_face_ids(sanitized_name, sanitized_ids)
     return JSONResponse(
         content=({"success": True, "message": "Successfully deleted faces."}),
         status_code=200,
@@ -642,7 +677,11 @@ def transcribe_audio(request: Request, body: AudioTranscriptionBody):
 def get_classification_dataset(name: str):
     dataset_dict: dict[str, list[str]] = {}
 
-    dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "dataset")
+    sanitized_name = sanitize_path_component(name)
+    dataset_dir = safe_join(CLIPS_DIR, name, "dataset")
+
+    if sanitized_name is None or dataset_dir is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(dataset_dir):
         return JSONResponse(
@@ -664,8 +703,8 @@ def get_classification_dataset(name: str):
             dataset_dict[category_name].append(file)
 
     # Get training metadata
-    metadata = read_training_metadata(sanitize_filename(name))
-    current_image_count = get_dataset_image_count(sanitize_filename(name))
+    metadata = read_training_metadata(sanitized_name)
+    current_image_count = get_dataset_image_count(sanitized_name)
 
     if metadata is None:
         training_metadata = {
@@ -729,8 +768,8 @@ def get_custom_attributes(
         if object_type is not None and object_type not in model_objects:
             continue
 
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
-        if not os.path.exists(dataset_dir):
+        dataset_dir = safe_join(CLIPS_DIR, model_key, "dataset")
+        if dataset_dir is None or not os.path.exists(dataset_dir):
             continue
 
         attributes = []
@@ -760,7 +799,10 @@ def get_custom_attributes(
     The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
 )
 def get_classification_images(name: str):
-    train_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
+    train_dir = safe_join(CLIPS_DIR, name, "train")
+
+    if train_dir is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(train_dir):
         return JSONResponse(status_code=200, content=[])
@@ -831,15 +873,17 @@ def delete_classification_dataset_images(
 
     json: dict[str, Any] = body or {}
     list_of_ids = json.get("ids", "")
-    folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
+    sanitized_name = sanitize_path_component(name)
+    folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if sanitized_name is None or folder is None:
+        return invalid_name_response(name)
 
     deleted_count = 0
     for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
+        file_path = safe_join(folder, id)
 
-        if os.path.isfile(file_path):
+        if file_path and os.path.isfile(file_path):
             os.unlink(file_path)
             deleted_count += 1
 
@@ -850,7 +894,6 @@ def delete_classification_dataset_images(
     # This ensures the dataset is marked as changed after deletion
     # (even if the total count happens to be the same after adding and deleting)
     if deleted_count > 0:
-        sanitized_name = sanitize_filename(name)
         metadata = read_training_metadata(sanitized_name)
         if metadata:
             last_count = metadata.get("last_training_image_count", 0)
@@ -888,8 +931,8 @@ def reclassify_classification_image(
         )
 
     json: dict[str, Any] = body or {}
-    image_id = sanitize_filename(json.get("id", ""))
-    new_category = sanitize_filename(json.get("new_category", ""))
+    image_id = sanitize_path_component(json.get("id", ""))
+    new_category = sanitize_path_component(json.get("new_category", ""))
 
     if not image_id or not new_category:
         return JSONResponse(
@@ -913,10 +956,13 @@ def reclassify_classification_image(
             status_code=400,
         )
 
-    sanitized_name = sanitize_filename(name)
-    source_folder = os.path.join(
-        CLIPS_DIR, sanitized_name, "dataset", sanitize_filename(category)
-    )
+    sanitized_name = sanitize_path_component(name)
+    source_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+    target_folder = safe_join(CLIPS_DIR, name, "dataset", new_category)
+
+    if sanitized_name is None or source_folder is None or target_folder is None:
+        return invalid_name_response(name)
+
     source_file = os.path.join(source_folder, image_id)
 
     if not os.path.isfile(source_file):
@@ -933,7 +979,6 @@ def reclassify_classification_image(
     random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
     timestamp = datetime.datetime.now().timestamp()
     new_name = f"{new_category}-{timestamp}-{random_id}.png"
-    target_folder = os.path.join(CLIPS_DIR, sanitized_name, "dataset", new_category)
 
     os.makedirs(target_folder, exist_ok=True)
 
@@ -983,7 +1028,7 @@ def rename_classification_category(
         )
 
     json: dict[str, Any] = body or {}
-    new_category = sanitize_filename(json.get("new_category", ""))
+    new_category = sanitize_path_component(json.get("new_category", ""))
 
     if not new_category:
         return JSONResponse(
@@ -996,12 +1041,12 @@ def rename_classification_category(
             status_code=400,
         )
 
-    old_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(old_category)
-    )
-    new_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", new_category
-    )
+    sanitized_name = sanitize_path_component(name)
+    old_folder = safe_join(CLIPS_DIR, name, "dataset", old_category)
+    new_folder = safe_join(CLIPS_DIR, name, "dataset", new_category)
+
+    if sanitized_name is None or old_folder is None or new_folder is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(old_folder):
         return JSONResponse(
@@ -1030,7 +1075,6 @@ def rename_classification_category(
 
         # Mark dataset as ready to train by resetting training metadata
         # This ensures the dataset is marked as changed after renaming
-        sanitized_name = sanitize_filename(name)
         write_training_metadata(sanitized_name, 0)
 
         return JSONResponse(
@@ -1078,13 +1122,20 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
         )
 
     json: dict[str, Any] = body or {}
-    category = sanitize_filename(json.get("category", ""))
-    training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "train", training_file_name
+    category = sanitize_path_component(json.get("category", ""))
+    training_file_name = json.get("training_file", "")
+    training_file = (
+        safe_join(CLIPS_DIR, name, "train", training_file_name)
+        if training_file_name
+        else None
     )
 
-    if training_file_name and not os.path.isfile(training_file):
+    if category is None:
+        return invalid_name_response(json.get("category", ""))
+
+    if training_file_name and (
+        training_file is None or not os.path.isfile(training_file)
+    ):
         return JSONResponse(
             content=(
                 {
@@ -1098,9 +1149,10 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
     random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
     timestamp = datetime.datetime.now().timestamp()
     new_name = f"{category}-{timestamp}-{random_id}.png"
-    new_file_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", category
-    )
+    new_file_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if new_file_folder is None:
+        return invalid_name_response(name)
 
     os.makedirs(new_file_folder, exist_ok=True)
 
@@ -1138,9 +1190,10 @@ def create_classification_category(request: Request, name: str, category: str):
             status_code=404,
         )
 
-    category_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
+    category_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if category_folder is None:
+        return invalid_name_response(category)
 
     os.makedirs(category_folder, exist_ok=True)
 
@@ -1179,12 +1232,15 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
 
     json: dict[str, Any] = body or {}
     list_of_ids = json.get("ids", "")
-    folder = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
+    folder = safe_join(CLIPS_DIR, name, "train")
+
+    if folder is None:
+        return invalid_name_response(name)
 
     for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
+        file_path = safe_join(folder, id)
 
-        if os.path.isfile(file_path):
+        if file_path and os.path.isfile(file_path):
             os.unlink(file_path)
 
     return JSONResponse(
@@ -1201,7 +1257,11 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
 )
 async def generate_state_examples(request: Request, body: GenerateStateExamplesBody):
     """Generate examples for state classification."""
-    model_name = sanitize_filename(body.model_name)
+    model_name = sanitize_path_component(body.model_name)
+
+    if model_name is None:
+        return invalid_name_response(body.model_name)
+
     cameras_normalized = {
         camera_name: tuple(crop)
         for camera_name, crop in body.cameras.items()
@@ -1224,7 +1284,11 @@ async def generate_state_examples(request: Request, body: GenerateStateExamplesB
 )
 async def generate_object_examples(request: Request, body: GenerateObjectExamplesBody):
     """Generate examples for object classification."""
-    model_name = sanitize_filename(body.model_name)
+    model_name = sanitize_path_component(body.model_name)
+
+    if model_name is None:
+        return invalid_name_response(body.model_name)
+
     collect_object_classification_examples(model_name, body.label)
 
     return JSONResponse(
@@ -1243,10 +1307,16 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
     Returns a success message.""",
 )
 def delete_classification_model(request: Request, name: str):
-    sanitized_name = sanitize_filename(name)
+    # This endpoint intentionally accepts models that are not in the config, so
+    # there is no allow list to fall back on. Both paths below are recursive
+    # deletes, so an unusable name has to be rejected outright.
+    data_dir = safe_join(CLIPS_DIR, name)
+    model_dir = safe_join(MODEL_CACHE_DIR, name)
+
+    if data_dir is None or model_dir is None:
+        return invalid_name_response(name)
 
     # Delete the classification model's data directory in clips
-    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
     if os.path.exists(data_dir):
         try:
             shutil.rmtree(data_dir)
@@ -1255,7 +1325,6 @@ def delete_classification_model(request: Request, name: str):
             logger.debug(f"Failed to delete data directory for {name}: {e}")
 
     # Delete the classification model's files in model_cache
-    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
     if os.path.exists(model_dir):
         try:
             shutil.rmtree(model_dir)

frigate/api/config_util.py

@@ -3,6 +3,30 @@
 from fastapi import FastAPI
 
 from frigate.config import FrigateConfig
+from frigate.config.camera.updater import (
+    CameraConfigUpdateEnum,
+    CameraConfigUpdateTopic,
+)
+
+
+def publish_camera_section_updates(
+    app: FastAPI, config: FrigateConfig, update_type: CameraConfigUpdateEnum
+) -> None:
+    """Broadcast every camera's re-resolved value for a global section.
+
+    Global sections are folded into each camera at parse time and the camera
+    copies are what workers read, so send them rather than leave a worker to
+    guess which cameras were inheriting.
+    """
+    for camera_name, camera_config in config.cameras.items():
+        settings = getattr(camera_config, update_type.name, None)
+
+        if settings is None:
+            continue
+
+        app.config_publisher.publish_update(
+            CameraConfigUpdateTopic(update_type, camera_name), settings
+        )
 
 
 def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
@@ -16,6 +40,10 @@ def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
     camera the user turned off would silently come back on.
     """
     app.frigate_config = config
+
+    if app.config_holder is not None:
+        app.config_holder.set(config)
+
     app.genai_manager.update_config(config)
 
     if app.profile_manager is not None:

frigate/api/event.py

@@ -16,7 +16,6 @@ import numpy as np
 from fastapi import APIRouter, Request
 from fastapi.params import Depends
 from fastapi.responses import JSONResponse
-from pathvalidate import sanitize_filename
 from peewee import JOIN, DoesNotExist, fn, operator
 from playhouse.shortcuts import model_to_dict
 
@@ -56,11 +55,12 @@ from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.comms.event_metadata_updater import EventMetadataTypeEnum
 from frigate.config.classification import ObjectClassificationType
-from frigate.const import CLIPS_DIR, TRIGGER_DIR
+from frigate.const import CLIPS_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event, ReviewSegment, Timeline, Trigger
 from frigate.track.object_processing import TrackedObject
 from frigate.util.file import get_event_thumbnail_bytes, load_event_snapshot_image
+from frigate.util.path import get_trigger_thumbnail_path, safe_join
 from frigate.util.time import get_dst_transitions, get_tz_modifiers
 
 logger = logging.getLogger(__name__)
@@ -1452,10 +1452,10 @@ async def set_attributes(
             continue
 
         # Get available labels from dataset directory
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
+        dataset_dir = safe_join(CLIPS_DIR, model_key, "dataset")
         available_labels = set()
 
-        if os.path.exists(dataset_dir):
+        if dataset_dir and os.path.exists(dataset_dir):
             for category_name in os.listdir(dataset_dir):
                 category_dir = os.path.join(dataset_dir, category_name)
                 if os.path.isdir(category_dir):
@@ -1748,6 +1748,7 @@ async def delete_events(request: Request, body: EventsDeleteBody):
     NOTES:
     - Creating a manual event does not trigger an update to /events MQTT topic.
     - If a duration is set to null, the event will need to be ended manually by calling /events/{event_id}/end.
+    - The review item is an alert unless the label is listed in the camera's review -> detections -> labels config.
     """,
 )
 def create_event(
@@ -1958,18 +1959,13 @@ def create_trigger_embedding(
         if body.type == "thumbnail":
             # Save image to the triggers directory
             try:
-                os.makedirs(
-                    os.path.join(TRIGGER_DIR, sanitize_filename(camera_name)),
-                    exist_ok=True,
-                )
-                with open(
-                    os.path.join(
-                        TRIGGER_DIR,
-                        sanitize_filename(camera_name),
-                        f"{sanitize_filename(body.data)}.webp",
-                    ),
-                    "wb",
-                ) as f:
+                webp_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+                if webp_path is None:
+                    raise ValueError(f"Invalid trigger thumbnail path for {body.data}")
+
+                os.makedirs(os.path.dirname(webp_path), exist_ok=True)
+                with open(webp_path, "wb") as f:
                     f.write(thumbnail)
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
@@ -2041,10 +2037,16 @@ def update_trigger_embedding(
         if body.type == "description":
             embedding = context.generate_description_embedding(body.data)
         elif body.type == "thumbnail":
-            webp_file = sanitize_filename(body.data) + ".webp"
-            webp_path = os.path.join(
-                TRIGGER_DIR, sanitize_filename(camera_name), webp_file
-            )
+            webp_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+            if webp_path is None:
+                return JSONResponse(
+                    content={
+                        "success": False,
+                        "message": f"Invalid data for {body.type} trigger",
+                    },
+                    status_code=400,
+                )
 
             try:
                 event: Event = Event.get(Event.id == body.data)
@@ -2101,13 +2103,14 @@ def update_trigger_embedding(
             # Update existing trigger
             if trigger.data != body.data:  # Delete old thumbnail only if data changes
                 try:
-                    os.remove(
-                        os.path.join(
-                            TRIGGER_DIR,
-                            sanitize_filename(camera_name),
-                            f"{trigger.data}.webp",
+                    old_path = get_trigger_thumbnail_path(camera_name, trigger.data)
+
+                    if old_path is None:
+                        raise ValueError(
+                            f"Invalid trigger thumbnail path for {trigger.data}"
                         )
-                    )
+
+                    os.remove(old_path)
                     logger.debug(
                         f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
                     )
@@ -2141,12 +2144,13 @@ def update_trigger_embedding(
         if body.type == "thumbnail":
             # Save image to the triggers directory
             try:
-                camera_path = os.path.join(TRIGGER_DIR, sanitize_filename(camera_name))
-                os.makedirs(camera_path, exist_ok=True)
-                with open(
-                    os.path.join(camera_path, f"{sanitize_filename(body.data)}.webp"),
-                    "wb",
-                ) as f:
+                thumbnail_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+                if thumbnail_path is None:
+                    raise ValueError(f"Invalid trigger thumbnail path for {body.data}")
+
+                os.makedirs(os.path.dirname(thumbnail_path), exist_ok=True)
+                with open(thumbnail_path, "wb") as f:
                     f.write(thumbnail)
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
@@ -2217,11 +2221,12 @@ def delete_trigger_embedding(
             )
 
         try:
-            os.remove(
-                os.path.join(
-                    TRIGGER_DIR, sanitize_filename(camera_name), f"{trigger.data}.webp"
-                )
-            )
+            thumbnail_path = get_trigger_thumbnail_path(camera_name, trigger.data)
+
+            if thumbnail_path is None:
+                raise ValueError(f"Invalid trigger thumbnail path for {trigger.data}")
+
+            os.remove(thumbnail_path)
             logger.debug(
                 f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
             )

frigate/api/export.py

@@ -13,7 +13,7 @@ from pathlib import Path
 import psutil
 from fastapi import APIRouter, Depends, Query, Request
 from fastapi.responses import JSONResponse, StreamingResponse
-from pathvalidate import sanitize_filename, sanitize_filepath
+from pathvalidate import sanitize_filename
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
@@ -72,6 +72,7 @@ from frigate.record.export import (
     PlaybackSourceEnum,
     validate_ffmpeg_args,
 )
+from frigate.util.path import sanitize_contained_path
 from frigate.util.time import is_current_hour
 
 logger = logging.getLogger(__name__)
@@ -129,18 +130,12 @@ def _validate_export_case(export_case_id: str | None) -> JSONResponse | None:
 def _sanitize_existing_image(
     image_path: str | None,
 ) -> tuple[str | None, JSONResponse | None]:
-    # sanitize_filepath normalizes "\" to "/" but leaves ".." intact, so a path
-    # like "clips\..\..\etc/passwd" passes the CLIPS_DIR prefix check yet still
-    # escapes the directory once resolved. A valid snapshot path never uses "..".
-    if image_path and ".." in image_path:
-        return None, JSONResponse(
-            content={"success": False, "message": "Invalid image path"},
-            status_code=400,
-        )
+    if not image_path:
+        return None, None
 
-    existing_image = sanitize_filepath(image_path) if image_path else None
+    existing_image = sanitize_contained_path(image_path, CLIPS_DIR)
 
-    if existing_image and not existing_image.startswith(CLIPS_DIR):
+    if existing_image is None:
         return None, JSONResponse(
             content={"success": False, "message": "Invalid image path"},
             status_code=400,

frigate/api/fastapi_app.py

@@ -35,6 +35,7 @@ from frigate.comms.event_metadata_updater import (
 )
 from frigate.config import FrigateConfig
 from frigate.config.camera.updater import CameraConfigUpdatePublisher
+from frigate.config.holder import ConfigHolder
 from frigate.config.profile_manager import ProfileManager
 from frigate.debug_replay import DebugReplayManager, debug_replay_auto_stop_watchdog
 from frigate.embeddings import EmbeddingsContext
@@ -74,6 +75,7 @@ def create_fastapi_app(
     dispatcher: Dispatcher | None = None,
     profile_manager: ProfileManager | None = None,
     enforce_default_admin: bool = True,
+    config_holder: ConfigHolder | None = None,
 ):
     logger.info("Starting FastAPI app")
     app = FastAPI(
@@ -150,6 +152,8 @@ def create_fastapi_app(
     app.include_router(debug_replay.router)
     # App Properties
     app.frigate_config = frigate_config
+    # snapshot the port nginx bound at startup, the live config can be swapped
+    app.auth_internal_port = frigate_config.networking.listen.internal_port
     app.genai_manager = GenAIClientManager(frigate_config)
     app.embeddings = embeddings
     app.detected_frames_processor = detected_frames_processor
@@ -162,6 +166,7 @@ def create_fastapi_app(
     app.replay_manager = replay_manager
     app.dispatcher = dispatcher
     app.profile_manager = profile_manager
+    app.config_holder = config_holder
 
     if frigate_config.auth.enabled:
         secret = get_jwt_secret()

frigate/api/media.py

@@ -53,6 +53,7 @@ from frigate.util.file import (
 )
 from frigate.util.image import get_image_from_recording, get_image_quality_params
 from frigate.util.media import get_keyframe_before
+from frigate.util.object import create_empty_regions_grid
 
 logger = logging.getLogger(__name__)
 
@@ -574,11 +575,11 @@ async def vod_ts(
             Recordings.start_time,
         )
         .where(
-            Recordings.start_time.between(start_ts, end_ts)
-            | Recordings.end_time.between(start_ts, end_ts)
-            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
+            Recordings.camera == camera_name,
+            Recordings.start_time >= start_ts - MAX_SEGMENT_DURATION,
+            Recordings.start_time <= end_ts,
+            Recordings.end_time >= start_ts,
         )
-        .where(Recordings.camera == camera_name)
         .order_by(Recordings.start_time.asc())
         .iterator()
     )
@@ -813,7 +814,7 @@ async def event_snapshot(
             timestamp_style=request.app.frigate_config.cameras[
                 event.camera
             ].timestamp_style,
-            colormap=request.app.frigate_config.model.colormap,
+            colormap=request.app.frigate_config.model_for_camera(event.camera).colormap,
         )
     except DoesNotExist:
         # see if the object is currently being tracked
@@ -1083,7 +1084,21 @@ def clear_region_grid(request: Request, camera_name: str):
             status_code=404,
         )
 
-    Regions.delete().where(Regions.camera == camera_name).execute()
+    # store an empty grid instead of deleting the row so the grid is
+    # rebuilt from newly tracked objects and not from all past history
+    region = {
+        Regions.camera: camera_name,
+        Regions.grid: create_empty_regions_grid(),
+        Regions.last_update: datetime.now().timestamp(),
+    }
+    (
+        Regions.insert(region)
+        .on_conflict(
+            conflict_target=[Regions.camera],
+            update=region,
+        )
+        .execute()
+    )
     return JSONResponse(
         content={"success": True, "message": "Region grid cleared"},
     )

frigate/api/motion_search.py

@@ -182,7 +182,7 @@ async def get_motion_search_status_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,
@@ -253,7 +253,7 @@ async def cancel_motion_search_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,

frigate/api/record.py

@@ -25,7 +25,7 @@ from frigate.api.defs.query.recordings_query_parameters import (
 )
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
-from frigate.const import RECORD_DIR
+from frigate.const import MAX_SEGMENT_DURATION, RECORD_DIR
 from frigate.models import Event, Recordings
 from frigate.util.time import get_dst_transitions
 
@@ -243,6 +243,7 @@ async def recordings(
         )
         .where(
             Recordings.camera == camera_name,
+            Recordings.start_time >= after - MAX_SEGMENT_DURATION,
             Recordings.end_time >= after,
             Recordings.start_time <= before,
         )

frigate/app.py

@@ -30,6 +30,7 @@ from frigate.comms.ws import WebSocketClient
 from frigate.comms.zmq_proxy import ZmqProxy
 from frigate.config.camera.updater import CameraConfigUpdatePublisher
 from frigate.config.config import FrigateConfig
+from frigate.config.holder import ConfigHolder
 from frigate.config.profile_manager import ProfileManager
 from frigate.const import (
     CACHE_DIR,
@@ -48,6 +49,8 @@ from frigate.debug_replay import (
     DebugReplayManager,
     cleanup_replay_cameras,
 )
+from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.device import build_detector_config, runner_names
 from frigate.embeddings import EmbeddingProcess, EmbeddingsContext
 from frigate.events.audio import AudioProcessor
 from frigate.events.cleanup import EventCleanup
@@ -68,6 +71,7 @@ from frigate.models import (
     User,
 )
 from frigate.object_detection.base import ObjectDetectProcess
+from frigate.object_detection.util import detection_frame_size
 from frigate.output.output import OutputProcess
 from frigate.ptz.autotrack import PtzAutoTrackerThread
 from frigate.ptz.onvif import OnvifController
@@ -97,32 +101,32 @@ class FrigateApp:
         self.metrics_manager = manager
         self.audio_process: mp.Process | None = None
         self.stop_event = stop_event
-        self.detection_queue: Queue = mp.Queue()
+        self.detection_queues: dict[SceneEnum, Queue] = {
+            model.scene: mp.Queue() for model in config.models
+        }
         self.detectors: dict[str, ObjectDetectProcess] = {}
         self.detection_shms: list[mp.shared_memory.SharedMemory] = []
         self.log_queue: Queue = mp.Queue()
         self.camera_metrics: DictProxy = self.metrics_manager.dict()
-        self.embeddings_metrics: DataProcessorMetrics | None = (
-            DataProcessorMetrics(
-                self.metrics_manager, list(config.classification.custom.keys())
-            )
-            if (
-                config.semantic_search.enabled
-                or any(
-                    c.objects.genai.enabled or c.review.genai.enabled
-                    for c in config.cameras.values()
-                )
-                or config.lpr.enabled
-                or config.face_recognition.enabled
-                or len(config.classification.custom) > 0
-            )
-            else None
+
+        self.embeddings_metrics = DataProcessorMetrics(
+            self.metrics_manager, list(config.classification.custom.keys())
         )
         self.ptz_metrics: dict[str, PTZMetrics] = {}
         self.processes: dict[str, int] = {}
         self.embeddings: EmbeddingsContext | None = None
-        self.profile_manager: ProfileManager | None = None
-        self.config = config
+        self.config_holder = ConfigHolder(config)
+
+    @property
+    def config(self) -> FrigateConfig:
+        """The current config, not the one Frigate booted with.
+
+        Read through the holder so the deferred watchdog factories below build
+        a replacement process from the config as it is now. There is no setter
+        on purpose: a plain attribute would let a caller pin this back to a
+        single object and reintroduce the staleness.
+        """
+        return self.config_holder.config
 
     def ensure_dirs(self) -> None:
         dirs = [
@@ -343,40 +347,20 @@ class FrigateApp:
         )
         self.dispatcher.profile_manager = self.profile_manager
 
-    def restore_active_profile(self) -> None:
-        """Re-activate the persisted profile after subscribers are connected.
-
-        ZMQ PUB/SUB drops messages with no subscribers, so activation must
-        run after every config_updater subscriber is up.
-        """
-        if self.profile_manager is None:
-            return
-
-        persisted = ProfileManager.load_persisted_profile()
-        if persisted and any(
-            persisted in cam.profiles for cam in self.config.cameras.values()
-        ):
-            logger.info("Restoring persisted profile '%s'", persisted)
-            # runtime overrides are layered on top via restore_runtime_state()
-            self.profile_manager.activate_profile(
-                persisted, clear_runtime_overrides=False
-            )
-
     def start_detectors(self) -> None:
+        model_cameras: dict[SceneEnum, list[str]] = {
+            model.scene: [] for model in self.config.models
+        }
+
         for name in self.config.cameras.keys():
+            model = self.config.model_for_camera(name)
+            model_cameras[model.scene].append(name)
+
             try:
-                largest_frame = max(
-                    [
-                        det.model.height * det.model.width * 3
-                        if det.model is not None
-                        else 320
-                        for det in self.config.detectors.values()
-                    ]
-                )
                 shm_in = UntrackedSharedMemory(
                     name=name,
                     create=True,
-                    size=largest_frame,
+                    size=detection_frame_size(model),
                 )
             except FileExistsError:
                 shm_in = UntrackedSharedMemory(name=name)
@@ -391,15 +375,26 @@ class FrigateApp:
             self.detection_shms.append(shm_in)
             self.detection_shms.append(shm_out)
 
-        for name, detector_config in self.config.detectors.items():
-            self.detectors[name] = ObjectDetectProcess(
-                name,
-                self.detection_queue,
-                list(self.config.cameras.keys()),
-                self.config,
-                detector_config,
-                self.stop_event,
-            )
+        # a device may be listed more than once to run additional inference
+        # processes on it, so names are only unique once de-duplicated
+        all_devices = [
+            device
+            for model in self.config.models
+            for device in self.config.devices_for_model(model)
+        ]
+        names = iter(runner_names(all_devices))
+
+        for model in self.config.models:
+            for device in self.config.devices_for_model(model):
+                name = next(names)
+                self.detectors[name] = ObjectDetectProcess(
+                    name,
+                    self.detection_queues[model.scene],
+                    model_cameras[model.scene],
+                    self.config,
+                    build_detector_config(device, model),
+                    self.stop_event,
+                )
 
     def start_ptz_autotracker(self) -> None:
         self.ptz_autotracker_thread = PtzAutoTrackerThread(
@@ -430,7 +425,7 @@ class FrigateApp:
     def start_camera_processor(self) -> None:
         self.camera_maintainer = CameraMaintainer(
             self.config,
-            self.detection_queue,
+            self.detection_queues,
             self.detected_frames_queue,
             self.camera_metrics,
             self.ptz_metrics,
@@ -610,6 +605,13 @@ class FrigateApp:
         self.start_detectors()
         self.init_dispatcher()
         self.init_profile_manager()
+
+        # workers get a copy of the config and can miss the broadcast below, so
+        # apply both layers here. must stay after init_profile_manager(), which
+        # snapshots the base config that profile deactivation resets to
+        self.profile_manager.restore_persisted_profile_to_config()
+        self.dispatcher.reapply_runtime_state_to_config()
+
         self.init_embeddings_client()
         self.start_video_output_processor()
         self.start_ptz_autotracker()
@@ -624,8 +626,9 @@ class FrigateApp:
         self.start_record_cleanup()
         self.start_watchdog()
 
-        # restore persisted runtime overrides on top of config
-        self.restore_active_profile()
+        # publish for the recording/review/embeddings processes, which start
+        # before the config can be corrected, and for the retained MQTT states
+        self.profile_manager.restore_persisted_profile()
         self.dispatcher.restore_runtime_state()
 
         self.init_auth()
@@ -645,6 +648,7 @@ class FrigateApp:
                     self.replay_manager,
                     self.dispatcher,
                     self.profile_manager,
+                    config_holder=self.config_holder,
                 ),
                 host="127.0.0.1",
                 port=5001,
@@ -685,8 +689,10 @@ class FrigateApp:
         for detector in self.detectors.values():
             detector.stop()
 
-        empty_and_close_queue(self.detection_queue)
-        logger.info("Detection queue closed")
+        for detection_queue in self.detection_queues.values():
+            empty_and_close_queue(detection_queue)
+
+        logger.info("Detection queues closed")
 
         self.detected_frames_processor.join()
         empty_and_close_queue(self.detected_frames_queue)

frigate/camera/activity_manager.py

@@ -18,6 +18,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
+from frigate.detectors.detector_config import NON_LOGO_ATTRIBUTES
 
 logger = logging.getLogger(__name__)
 
@@ -178,7 +179,7 @@ class CameraActivityManager:
             return
 
         for label in camera_config.objects.track:
-            if label in self.config.model.non_logo_attributes:
+            if label in NON_LOGO_ATTRIBUTES:
                 continue
 
             new_count = all_objects[label]

frigate/camera/maintainer.py

@@ -15,7 +15,9 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateSubscriber,
 )
 from frigate.const import REPLAY_CAMERA_PREFIX
+from frigate.detectors.detector_config import SceneEnum
 from frigate.models import Regions
+from frigate.object_detection.util import detection_frame_size
 from frigate.util.builtin import empty_and_close_queue
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
 from frigate.util.object import get_camera_regions_grid
@@ -29,7 +31,7 @@ class CameraMaintainer(threading.Thread):
     def __init__(
         self,
         config: FrigateConfig,
-        detection_queue: Queue,
+        detection_queues: dict[SceneEnum, Queue],
         detected_frames_queue: Queue,
         camera_metrics: DictProxy,
         ptz_metrics: dict[str, PTZMetrics],
@@ -38,7 +40,7 @@ class CameraMaintainer(threading.Thread):
     ):
         super().__init__(name="camera_processor")
         self.config = config
-        self.detection_queue = detection_queue
+        self.detection_queues = detection_queues
         self.detected_frames_queue = detected_frames_queue
         self.stop_event = stop_event
         self.camera_metrics = camera_metrics
@@ -79,10 +81,11 @@ class CameraMaintainer(threading.Thread):
         # create or update region grids for each camera
         for camera in self.config.cameras.values():
             assert camera.name is not None
+            model = self.config.model_for_camera(camera.name)
             self.region_grids[camera.name] = get_camera_regions_grid(
                 camera.name,
                 camera.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
             )
 
     def __calculate_shm_frame_count(self) -> int:
@@ -114,6 +117,7 @@ class CameraMaintainer(threading.Thread):
             return
 
         camera_stop_event = self.__ensure_camera_stop_event(name)
+        model = self.config.model_for_camera(name)
 
         if runtime:
             self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
@@ -123,32 +127,24 @@ class CameraMaintainer(threading.Thread):
             self.region_grids[name] = get_camera_regions_grid(
                 name,
                 config.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
             )
 
             try:
-                largest_frame = max(
-                    [
-                        det.model.height * det.model.width * 3
-                        if det.model is not None
-                        else 320
-                        for det in self.config.detectors.values()
-                    ]
-                )
                 UntrackedSharedMemory(name=f"out-{name}", create=True, size=20 * 6 * 4)
                 UntrackedSharedMemory(
                     name=name,
                     create=True,
-                    size=largest_frame,
+                    size=detection_frame_size(model),
                 )
             except FileExistsError:
                 pass
 
         camera_process = CameraTracker(
             config,
-            self.config.model,
-            self.config.model.merged_labelmap,
-            self.detection_queue,
+            model,
+            model.merged_labelmap,
+            self.detection_queues[model.scene],
             self.detected_frames_queue,
             self.camera_metrics[name],
             self.ptz_metrics[name],

frigate/camera/state.py

@@ -40,6 +40,7 @@ class CameraState:
         self.name = name
         self.config = config
         self.camera_config = config.cameras[name]
+        self.model = config.model_for_camera(name)
         self.frame_manager = frame_manager
         self.best_objects: dict[str, TrackedObject] = {}
         self.tracked_objects: dict[str, TrackedObject] = {}
@@ -101,9 +102,7 @@ class CameraState:
                         thickness = 1
                     else:
                         thickness = 2
-                        color = self.config.model.colormap.get(
-                            obj["label"], (255, 255, 255)
-                        )
+                        color = self.model.colormap.get(obj["label"], (255, 255, 255))
                 else:
                     thickness = 1
                     color = (255, 0, 0)
@@ -125,9 +124,7 @@ class CameraState:
                     and obj["frame_time"] == frame_time
                 ):
                     thickness = 5
-                    color = self.config.model.colormap.get(
-                        obj["label"], (255, 255, 255)
-                    )
+                    color = self.model.colormap.get(obj["label"], (255, 255, 255))
 
                     # debug autotracking zooming - show the zoom factor box
                     if (
@@ -261,9 +258,7 @@ class CameraState:
         if draw_options.get("paths"):
             for obj in tracked_objects.values():
                 if obj["frame_time"] == frame_time and obj["path_data"]:
-                    color = self.config.model.colormap.get(
-                        obj["label"], (255, 255, 255)
-                    )
+                    color = self.model.colormap.get(obj["label"], (255, 255, 255))
 
                     path_points = [
                         (
@@ -366,7 +361,7 @@ class CameraState:
         for id in new_ids:
             logger.debug(f"{self.name}: New tracked object ID: {id}")
             new_obj = tracked_objects[id] = TrackedObject(
-                self.config.model,
+                self.model,
                 self.camera_config,
                 self.config.ui,
                 self.frame_cache,
@@ -510,7 +505,7 @@ class CameraState:
                 sub_label = None
 
                 if obj.obj_data.get("sub_label"):
-                    if obj.obj_data["sub_label"][0] in self.config.model.all_attributes:
+                    if obj.obj_data["sub_label"][0] in self.model.all_attributes:
                         label = obj.obj_data["sub_label"][0]
                     else:
                         label = f"{object_type}-verified"

frigate/comms/dispatcher.py

@@ -11,7 +11,7 @@ from frigate.camera.activity_manager import AudioActivityManager, CameraActivity
 from frigate.comms.base_communicator import Communicator
 from frigate.comms.runtime_state import RuntimeStatePersistence
 from frigate.comms.webpush import WebPushClient
-from frigate.config import BirdseyeModeEnum, FrigateConfig
+from frigate.config import BirdseyeModeConfig, FrigateConfig
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdatePublisher,
@@ -156,10 +156,11 @@ class Dispatcher:
             if camera not in self.config.cameras:
                 return None
 
+            model = self.config.model_for_camera(camera)
             grid = get_camera_regions_grid(
                 camera,
                 self.config.cameras[camera].detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
             )
             return grid
 
@@ -882,8 +883,9 @@ class Dispatcher:
     def _on_birdseye_mode_command(self, camera_name: str, payload: str) -> None:
         """Callback for birdseye mode topic."""
 
-        if payload not in ["CONTINUOUS", "MOTION", "OBJECTS"]:
-            logger.info(f"Invalid birdseye_mode command: {payload}")
+        mode = BirdseyeModeConfig.from_mqtt_payload(payload)
+        if mode is None:
+            logger.info("Invalid birdseye_mode command: %s", payload)
             return
 
         birdseye_settings = self.config.cameras[camera_name].birdseye
@@ -892,7 +894,7 @@ class Dispatcher:
             logger.info(f"Birdseye mode not enabled for {camera_name}")
             return
 
-        birdseye_settings.mode = BirdseyeModeEnum(payload.lower())
+        birdseye_settings.mode = mode
         logger.info(
             f"Setting birdseye mode for {camera_name} to {birdseye_settings.mode}"
         )
@@ -901,7 +903,9 @@ class Dispatcher:
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.birdseye, camera_name),
             birdseye_settings,
         )
-        self.publish(f"{camera_name}/birdseye_mode/state", payload, retain=True)
+        self.publish(
+            f"{camera_name}/birdseye_mode/state", mode.to_mqtt_payload(), retain=True
+        )
 
     def _on_camera_notification_command(self, camera_name: str, payload: str) -> None:
         """Callback for camera level notifications topic."""

frigate/comms/mqtt.py

@@ -77,6 +77,11 @@ class MqttClient(Communicator):
                 "ON" if camera.audio.enabled_in_config else "OFF",
                 retain=True,
             )
+            self.publish(
+                f"{camera_name}/audio_transcription/state",
+                "ON" if camera.audio_transcription.live_enabled else "OFF",
+                retain=True,
+            )
             self.publish(
                 f"{camera_name}/detect/state",
                 "ON" if camera.detect.enabled else "OFF",
@@ -120,7 +125,7 @@ class MqttClient(Communicator):
             self.publish(
                 f"{camera_name}/birdseye_mode/state",
                 (
-                    camera.birdseye.mode.value.upper()
+                    camera.birdseye.mode.to_mqtt_payload()
                     if camera.birdseye.enabled
                     else "OFF"
                 ),
@@ -258,6 +263,7 @@ class MqttClient(Communicator):
             "snapshots",
             "detect",
             "audio",
+            "audio_transcription",
             "motion",
             "improve_contrast",
             "ptz_autotracker",

frigate/config/camera/birdseye.py

@@ -1,5 +1,3 @@
-from enum import Enum
-
 from pydantic import BaseModel, Field
 
 from ..base import FrigateBaseModel
@@ -8,22 +6,78 @@ __all__ = [
     "BirdseyeCameraConfig",
     "BirdseyeConfig",
     "BirdseyeLayoutConfig",
-    "BirdseyeModeEnum",
+    "BirdseyeModeConfig",
 ]
 
+BIRDSEYE_ACTIVITY_TYPES = (
+    "objects",
+    "motion",
+    "stationary_objects",
+    "continuous",
+)
 
-class BirdseyeModeEnum(str, Enum):
-    objects = "objects"
-    motion = "motion"
-    continuous = "continuous"
+
+class BirdseyeModeConfig(FrigateBaseModel):
+    continuous: bool = Field(
+        default=False,
+        title="Continuous",
+        description="Always include the camera in Birdseye.",
+    )
+    motion: bool = Field(
+        default=False,
+        title="Motion",
+        description="Include the camera in Birdseye when motion is detected.",
+    )
+    objects: bool = Field(
+        default=False,
+        title="Active objects",
+        description="Include the camera in Birdseye while an active object is tracked.",
+    )
+    stationary_objects: bool = Field(
+        default=False,
+        title="Stationary objects",
+        description="Include the camera in Birdseye while a stationary object is tracked.",
+    )
 
     @classmethod
-    def get_index(cls, type):
-        return list(cls).index(type)
+    def from_mqtt_payload(cls, payload: str) -> "BirdseyeModeConfig | None":
+        """Create mode options from an uppercase MQTT payload."""
+        raw_modes = payload.split(",")
+        if not raw_modes or any(not mode for mode in raw_modes):
+            return None
 
-    @classmethod
-    def get(cls, index):
-        return list(cls)[index]
+        modes = [mode.lower() for mode in raw_modes]
+        if any(
+            raw_mode != mode.upper() or mode not in BIRDSEYE_ACTIVITY_TYPES
+            for raw_mode, mode in zip(raw_modes, modes)
+        ):
+            return None
+
+        if len(modes) != len(set(modes)):
+            return None
+
+        return cls(**{mode: True for mode in modes})
+
+    def has_enabled_activity(self) -> bool:
+        """Return whether at least one activity type is enabled."""
+        return any(getattr(self, activity) for activity in BIRDSEYE_ACTIVITY_TYPES)
+
+    def to_mqtt_payload(self) -> str:
+        """Serialize enabled mode options for MQTT state topics."""
+        payload = ",".join(
+            activity.upper()
+            for activity in BIRDSEYE_ACTIVITY_TYPES
+            if getattr(self, activity)
+        )
+        if not payload:
+            raise ValueError("At least one Birdseye activity type must be enabled")
+
+        return payload
+
+
+def default_birdseye_mode() -> BirdseyeModeConfig:
+    """Return the default Birdseye mode configuration."""
+    return BirdseyeModeConfig(objects=True)
 
 
 class BirdseyeLayoutConfig(FrigateBaseModel):
@@ -47,10 +101,10 @@ class BirdseyeConfig(FrigateBaseModel):
         title="Enable Birdseye",
         description="Enable or disable the Birdseye view feature.",
     )
-    mode: BirdseyeModeEnum = Field(
-        default=BirdseyeModeEnum.objects,
-        title="Tracking mode",
-        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
+    mode: BirdseyeModeConfig = Field(
+        default_factory=default_birdseye_mode,
+        title="Activity types",
+        description="Activity types that include cameras in Birdseye.",
     )
 
     restream: bool = Field(
@@ -102,10 +156,10 @@ class BirdseyeCameraConfig(BaseModel):
         title="Enable Birdseye",
         description="Enable or disable the Birdseye view feature.",
     )
-    mode: BirdseyeModeEnum = Field(
-        default=BirdseyeModeEnum.objects,
-        title="Tracking mode",
-        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
+    mode: BirdseyeModeConfig = Field(
+        default_factory=default_birdseye_mode,
+        title="Activity types",
+        description="Activity types that include cameras in Birdseye.",
     )
 
     order: int = Field(

frigate/config/camera/detect.py

@@ -1,5 +1,7 @@
 from pydantic import Field, model_validator
 
+from frigate.detectors.detector_config import SceneEnum
+
 from ..base import FrigateBaseModel
 
 __all__ = ["DetectConfig", "StationaryConfig", "StationaryMaxFramesConfig"]
@@ -60,6 +62,11 @@ class DetectConfig(FrigateBaseModel):
         title="Detect width",
         description="Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
     )
+    scene: SceneEnum | None = Field(
+        default=None,
+        title="Detect scene",
+        description="The environment this camera looks at, used to pick which of the configured models runs on it. Defaults to the model with a scene of 'all'.",
+    )
     fps: int = Field(
         default=5,
         title="Detect FPS",

frigate/config/camera/ui.py

@@ -13,8 +13,8 @@ class CameraUiConfig(FrigateBaseModel):
     )
     dashboard: bool = Field(
         default=True,
-        title="Show in UI",
-        description="Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again.",
+        title="Show on Live dashboard",
+        description="Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings.",
     )
     review: bool = Field(
         default=True,

frigate/config/config.py

@@ -11,7 +11,6 @@ from pydantic import (
     BaseModel,
     ConfigDict,
     Field,
-    TypeAdapter,
     ValidationInfo,
     field_validator,
     model_validator,
@@ -19,8 +18,9 @@ from pydantic import (
 from ruamel.yaml import YAML
 
 from frigate.const import REGEX_JSON
-from frigate.detectors import DetectorConfig, ModelConfig
-from frigate.detectors.detector_config import BaseDetectorConfig
+from frigate.detectors import ModelConfig
+from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.device import DeviceParseError, DeviceSpec, parse_device
 from frigate.plus import PlusApi
 from frigate.util.builtin import (
     deep_merge,
@@ -41,7 +41,7 @@ from .auth import AuthConfig
 from .base import FrigateBaseModel
 from .camera import CameraConfig, CameraLiveConfig
 from .camera.audio import AudioConfig, AudioFilterConfig
-from .camera.birdseye import BirdseyeConfig
+from .camera.birdseye import BirdseyeConfig, BirdseyeModeConfig
 from .camera.detect import DetectConfig
 from .camera.ffmpeg import FfmpegConfig
 from .camera.genai import GenAIConfig, GenAIRoleEnum
@@ -79,9 +79,14 @@ logger = logging.getLogger(__name__)
 
 yaml = YAML()
 
-# Pydantic field default applied when an existing config omits `detectors:`.
+# Pydantic field default applied when an existing config omits `models:`.
 # Kept as cpu tflite for backwards compatibility with 0.17 configs.
-DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
+DEFAULT_MODELS = [{"devices": ["cpu"]}]
+
+
+def _default_models() -> list[ModelConfig]:
+    return [ModelConfig.model_validate(model) for model in DEFAULT_MODELS]
+
 
 # Used by the openvino branch below and rendered into the new-config YAML
 # template so first-time setups default to openvino on CPU.
@@ -93,7 +98,7 @@ DEFAULT_MODEL = {
     "path": "/openvino-model/ssdlite_mobilenet_v2.xml",
     "labelmap_path": "/openvino-model/coco_91cl_bkgr.txt",
 }
-NEW_CONFIG_DETECTORS = {"ov": {"type": "openvino", "device": "CPU"}}
+NEW_CONFIG_MODELS = [{"devices": ["openvino:CPU"], **DEFAULT_MODEL}]
 DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}
 
 
@@ -109,7 +114,7 @@ DEFAULT_CONFIG = f"""
 mqtt:
   enabled: False
 
-{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "model": DEFAULT_MODEL})}
+{_render_default_yaml({"models": NEW_CONFIG_MODELS})}
 cameras: {{}}  # No cameras defined, UI wizard should be used
 version: {CURRENT_CONFIG_VERSION}
 """
@@ -326,8 +331,20 @@ def verify_required_zones_exist(camera_config: CameraConfig) -> None:
 
 
 def verify_profile_overrides_match_base(camera_config: CameraConfig) -> None:
-    """Verify that profile zone and mask IDs reference entries defined on the base camera."""
+    """Verify profile overrides against the resolved base camera configuration."""
     for profile_name, profile in camera_config.profiles.items():
+        if profile.birdseye is not None:
+            overrides = profile.birdseye.mode.model_dump(exclude_unset=True)
+            base_mode = camera_config.birdseye.mode.model_dump()
+            resolved_mode = BirdseyeModeConfig.model_validate(
+                deep_merge(overrides, base_mode)
+            )
+            if not resolved_mode.has_enabled_activity():
+                raise ValueError(
+                    f"Camera '{camera_config.name}' profile '{profile_name}' must "
+                    "enable at least one Birdseye activity type"
+                )
+
         if profile.zones:
             for zone_name in profile.zones:
                 if zone_name not in camera_config.zones:
@@ -497,16 +514,11 @@ class FrigateConfig(FrigateBaseModel):
         description="User interface preferences such as timezone, time/date formatting, and units.",
     )
 
-    # Detector config
-    detectors: dict[str, BaseDetectorConfig] = Field(
-        default=DEFAULT_DETECTORS,
-        title="Detector hardware",
-        description="Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
-    )
-    model: ModelConfig = Field(
-        default_factory=ModelConfig,
-        title="Detection model",
-        description="Settings to configure a custom object detection model and its input shape.",
+    # Detection model config
+    models: list[ModelConfig] = Field(
+        default_factory=_default_models,
+        title="Detection models",
+        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene.",
     )
 
     # GenAI config (named provider configs: name -> GenAIConfig)
@@ -621,11 +633,202 @@ class FrigateConfig(FrigateBaseModel):
     )
 
     _plus_api: PlusApi
+    _model_devices: dict[SceneEnum, list[DeviceSpec]]
+    _camera_models: dict[str, ModelConfig]
+    _all_attributes: list[str]
+    _all_attribute_logos: list[str]
+    _all_attributes_map: dict[str, list[str]]
+    _all_labels: set[str]
 
     @property
     def plus_api(self) -> PlusApi:
         return self._plus_api
 
+    @property
+    def all_attributes(self) -> list[str]:
+        """Every attribute label across all configured models."""
+        return self._all_attributes
+
+    @property
+    def all_attribute_logos(self) -> list[str]:
+        """Every logo attribute label across all configured models."""
+        return self._all_attribute_logos
+
+    @property
+    def all_attributes_map(self) -> dict[str, list[str]]:
+        """Object label to attribute labels, merged across all configured models."""
+        return self._all_attributes_map
+
+    @property
+    def all_labels(self) -> set[str]:
+        """Every object label across all configured models."""
+        return self._all_labels
+
+    @property
+    def primary_model(self) -> ModelConfig:
+        """The model used when no specific camera is in play."""
+        for model in self.models:
+            if model.scene == SceneEnum.all:
+                return model
+
+        return self.models[0]
+
+    def model_for_camera(self, camera_name: str) -> ModelConfig:
+        """Get the detection model a camera runs on.
+
+        Args:
+            camera_name: Name of the camera
+
+        Returns:
+            The model matching the camera's detect scene
+        """
+        return self._camera_models[camera_name]
+
+    def devices_for_model(self, model: ModelConfig) -> list[DeviceSpec]:
+        """Get the parsed hardware devices a model runs on.
+
+        Args:
+            model: One of the configured models
+
+        Returns:
+            The parsed device specs, in config order
+        """
+        return self._model_devices[model.scene]
+
+    def _load_model(self, model: ModelConfig, detector: str) -> ModelConfig:
+        """Apply detector specific defaults to a model and load its weights and labels.
+
+        Args:
+            model: The configured model
+            detector: The detector type the model runs on
+
+        Returns:
+            The loaded model
+        """
+        model_config = model.model_dump(exclude_unset=True, warnings="none")
+
+        if "path" not in model_config:
+            if detector == "cpu" or detector.endswith("_tfl"):
+                model_config["path"] = "/cpu_model.tflite"
+            elif detector == "edgetpu":
+                model_config["path"] = "/edgetpu_model.tflite"
+            elif detector == "openvino":
+                for default_key, default_value in DEFAULT_MODEL.items():
+                    model_config.setdefault(default_key, default_value)
+
+        loaded = ModelConfig.model_validate(model_config)
+        loaded.check_and_load_plus_model(self.plus_api, detector)
+        loaded.compute_model_hash()
+        return loaded
+
+    def _load_models(self) -> None:
+        """Validate the configured models and load each one."""
+        if not self.models:
+            raise ValueError("At least one model must be configured under models")
+
+        model_devices: dict[SceneEnum, list[DeviceSpec]] = {}
+        # device string -> the scene of the model that already claimed it
+        claimed_devices: dict[str, SceneEnum] = {}
+
+        for index, model in enumerate(self.models):
+            scene = model.scene.value
+
+            if model.scene in model_devices:
+                raise ValueError(
+                    f"Multiple models are configured with a scene of '{scene}'. Each model must use a different scene."
+                )
+
+            if not model.devices:
+                raise ValueError(
+                    f"Model '{scene}' must list at least one entry under devices."
+                )
+
+            try:
+                devices = [parse_device(device) for device in model.devices]
+            except DeviceParseError as err:
+                raise ValueError(
+                    f"Model '{scene}' has an invalid device: {err}"
+                ) from err
+
+            detectors = {device.detector for device in devices}
+
+            if len(detectors) > 1:
+                raise ValueError(
+                    f"Model '{scene}' mixes the {', '.join(sorted(detectors))} detectors. All of a model's devices must use the same detector."
+                )
+
+            for device in devices:
+                if device.raw in claimed_devices and not device.shareable:
+                    other = claimed_devices[device.raw]
+                    where = (
+                        f"twice by model '{scene}'"
+                        if other == model.scene
+                        else f"by both the '{other.value}' and '{scene}' models"
+                    )
+                    raise ValueError(
+                        f"Device '{device.raw}' is used {where}, but it can only run one detection process."
+                    )
+
+                claimed_devices[device.raw] = model.scene
+
+            self.models[index] = self._load_model(model, devices[0].detector)
+            model_devices[model.scene] = devices
+
+        attributes: set[str] = set()
+        attribute_logos: set[str] = set()
+        attributes_map: dict[str, set[str]] = {}
+        labels: set[str] = set()
+
+        for model in self.models:
+            attributes.update(model.all_attributes)
+            attribute_logos.update(model.all_attribute_logos)
+            labels.update(model.merged_labelmap.values())
+
+            for label, label_attributes in model.attributes_map.items():
+                attributes_map.setdefault(label, set()).update(label_attributes)
+
+        self._model_devices = model_devices
+        self._all_attributes = sorted(attributes)
+        self._all_attribute_logos = sorted(attribute_logos)
+        self._all_attributes_map = {
+            label: sorted(label_attributes)
+            for label, label_attributes in sorted(attributes_map.items())
+        }
+        self._all_labels = labels
+
+    def _resolve_camera_model(self, name: str, scene: SceneEnum | None) -> ModelConfig:
+        """Resolve which model a camera runs on.
+
+        Args:
+            name: Name of the camera
+            scene: The camera's configured detect scene, if any
+
+        Returns:
+            The model the camera runs on
+        """
+        by_scene = {model.scene: model for model in self.models}
+
+        if scene is not None:
+            model = by_scene.get(scene)
+
+            if model is None:
+                raise ValueError(
+                    f"Camera '{name}' has a detect scene of '{scene.value}', but no model is configured for that scene."
+                )
+
+            return model
+
+        default = by_scene.get(SceneEnum.all) or (
+            self.models[0] if len(self.models) == 1 else None
+        )
+
+        if default is None:
+            raise ValueError(
+                f"Camera '{name}' must set detect -> scene, because more than one model is configured and none of them uses a scene of 'all'."
+            )
+
+        return default
+
     @model_validator(mode="after")
     def post_validation(self, info: ValidationInfo) -> Self:
         # Load plus api from context, if possible.
@@ -670,8 +873,10 @@ class FrigateConfig(FrigateBaseModel):
                     "'embeddings' in its roles for semantic search."
                 )
 
+        self._load_models()
+
         # set default min_score for object attributes
-        for attribute in self.model.all_attributes:
+        for attribute in self.all_attributes:
             existing = self.objects.filters.get(attribute)
             if existing is None:
                 self.objects.filters[attribute] = FilterConfig(min_score=0.7)
@@ -721,44 +926,7 @@ class FrigateConfig(FrigateBaseModel):
             exclude_unset=True,
         )
 
-        for key, detector in self.detectors.items():
-            adapter = TypeAdapter(DetectorConfig)
-            model_dict = (
-                detector
-                if isinstance(detector, dict)
-                else detector.model_dump(warnings="none")
-            )
-            detector_config: BaseDetectorConfig = adapter.validate_python(model_dict)
-
-            # users should not set model themselves
-            if detector_config.model:
-                logger.warning(
-                    "The model key should be specified at the root level of the config, not under detectors. The nested model key will be ignored."
-                )
-                detector_config.model = None
-
-            model_config = self.model.model_dump(exclude_unset=True, warnings="none")
-
-            if detector_config.model_path:
-                model_config["path"] = detector_config.model_path
-
-            if "path" not in model_config:
-                if detector_config.type == "cpu" or detector_config.type.endswith(
-                    "_tfl"
-                ):
-                    model_config["path"] = "/cpu_model.tflite"
-                elif detector_config.type == "edgetpu":
-                    model_config["path"] = "/edgetpu_model.tflite"
-                elif detector_config.type == "openvino":
-                    for default_key, default_value in DEFAULT_MODEL.items():
-                        model_config.setdefault(default_key, default_value)
-
-            model = ModelConfig.model_validate(model_config)
-            model.check_and_load_plus_model(self.plus_api, detector_config.type)
-            model.compute_model_hash()
-            labelmap_objects = model.merged_labelmap.values()
-            detector_config.model = model
-            self.detectors[key] = detector_config
+        self._camera_models = {}
 
         for name, camera in self.cameras.items():
             modified_global_config = global_config.copy()
@@ -785,6 +953,9 @@ class FrigateConfig(FrigateBaseModel):
                 {"name": name, **merged_config}
             )
 
+            camera_model = self._resolve_camera_model(name, camera_config.detect.scene)
+            self._camera_models[name] = camera_model
+
             if camera_config.ffmpeg.hwaccel_args == "auto":
                 camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
 
@@ -998,6 +1169,10 @@ class FrigateConfig(FrigateBaseModel):
             self.cameras[name] = camera_config
 
             verify_config_roles(camera_config)
+            if not camera_config.birdseye.mode.has_enabled_activity():
+                raise ValueError(
+                    f"Camera '{name}' must enable at least one Birdseye activity type"
+                )
             verify_valid_live_stream_names(self, camera_config)
             verify_recording_segments_setup_with_reasonable_time(camera_config)
             verify_zone_objects_are_tracked(camera_config)
@@ -1005,7 +1180,7 @@ class FrigateConfig(FrigateBaseModel):
             verify_profile_overrides_match_base(camera_config)
             verify_autotrack_zones(camera_config)
             verify_motion_and_detect(camera_config)
-            verify_objects_track(camera_config, labelmap_objects)
+            verify_objects_track(camera_config, camera_model.merged_labelmap.values())
             verify_lpr_and_face(self, camera_config)
 
         # Validate camera profiles reference top-level profile definitions
@@ -1022,8 +1197,16 @@ class FrigateConfig(FrigateBaseModel):
             config.name = name
 
         self.objects.parse_all_objects(self.cameras)
-        self.model.create_colormap(sorted(self.objects.all_objects))
-        self.model.check_and_load_plus_model(self.plus_api)
+
+        # every model shares one colormap so a label is drawn the same color no
+        # matter which model detected it, so filter attributes across all models
+        # rather than letting each model filter with only its own
+        colored_labels = sorted(
+            set(self.objects.all_objects) - set(self.all_attributes)
+        )
+
+        for model in self.models:
+            model.create_colormap(colored_labels)
 
         # Check audio transcription and audio detection requirements
         if self.audio_transcription.enabled:

frigate/config/holder.py

@@ -0,0 +1,34 @@
+"""Shared handle on the config object that is current for this instance."""
+
+from .config import FrigateConfig
+
+__all__ = ["ConfigHolder"]
+
+
+class ConfigHolder:
+    """Indirection for the most recently parsed config.
+
+    /api/config/set re-parses yaml into a brand new FrigateConfig instead of
+    mutating the old one, so any reference captured during startup goes stale
+    the first time a user saves. Anything that has to build something after
+    startup, most importantly the watchdog factories that rebuild a crashed
+    process, must read through a holder rather than close over a config
+    object, or the rebuilt process comes back with the config as it was at
+    boot and silently discards every change made since.
+
+    There is deliberately no setter on the read side: the swap runs in exactly
+    one place (frigate.api.config_util.swap_runtime_config) and everyone else
+    only reads.
+    """
+
+    def __init__(self, config: FrigateConfig) -> None:
+        self._config = config
+
+    @property
+    def config(self) -> FrigateConfig:
+        """The config as of the most recent successful save."""
+        return self._config
+
+    def set(self, config: FrigateConfig) -> None:
+        """Install a freshly parsed config as the current one."""
+        self._config = config

frigate/config/network.py

@@ -1,10 +1,18 @@
-from pydantic import Field
+from pydantic import Field, model_validator
 
 from .base import FrigateBaseModel
 
 __all__ = ["IPv6Config", "ListenConfig", "NetworkingConfig"]
 
 
+def parse_listen_port(value: int | str) -> int:
+    """Return the port number from a bare port or an "address:port" value."""
+    if isinstance(value, str):
+        return int(value.split(":")[-1])
+
+    return value
+
+
 class IPv6Config(FrigateBaseModel):
     enabled: bool = Field(
         default=False,
@@ -25,6 +33,21 @@ class ListenConfig(FrigateBaseModel):
         description="External listening port for Frigate (default 8971).",
     )
 
+    @property
+    def internal_port(self) -> int:
+        return parse_listen_port(self.internal)
+
+    @property
+    def external_port(self) -> int:
+        return parse_listen_port(self.external)
+
+    @model_validator(mode="after")
+    def validate_distinct_ports(self) -> "ListenConfig":
+        if self.internal_port == self.external_port:
+            raise ValueError("internal and external must listen on different ports")
+
+        return self
+
 
 class NetworkingConfig(FrigateBaseModel):
     ipv6: IPv6Config = Field(

frigate/config/profile_manager.py

@@ -43,7 +43,9 @@ SECTION_STATE_TOPICS: dict[str, list[tuple[str, Callable[[Any], Any]]]] = {
         ("birdseye", lambda c: "ON" if c.birdseye.enabled else "OFF"),
         (
             "birdseye_mode",
-            lambda c: c.birdseye.mode.value.upper() if c.birdseye.enabled else "OFF",
+            lambda c: (
+                c.birdseye.mode.to_mqtt_payload() if c.birdseye.enabled else "OFF"
+            ),
         ),
     ],
     "detect": [("detect", lambda c: "ON" if c.detect.enabled else "OFF")],
@@ -169,6 +171,93 @@ class ProfileManager:
                 self.config.active_profile = None
                 self._persist_active_profile(None)
 
+    def _validate_profile_name(self, profile_name: str | None) -> str | None:
+        """Return an error message if the name is not a defined profile."""
+        if profile_name is not None and profile_name not in self.config.profiles:
+            return f"Profile '{profile_name}' is not defined in the profiles section"
+
+        return None
+
+    def _apply_to_config(
+        self, profile_name: str | None
+    ) -> tuple[dict[str, set[str]], str | None]:
+        """Reset every camera to base, then apply the named profile on top.
+
+        Returns the changed camera/section pairs, plus an error message if
+        applying the profile failed partway through.
+        """
+        changed: dict[str, set[str]] = {}
+
+        self._reset_to_base(changed)
+
+        if profile_name is not None:
+            err = self._apply_profile_overrides(profile_name, changed)
+            if err:
+                return changed, err
+
+        return changed, None
+
+    def apply_profile_to_config(self, profile_name: str | None) -> str | None:
+        """Apply a profile to the in-memory config, without publishing it.
+
+        Safe to call ahead of activate_profile: both reset to the base config
+        first, so the later call re-derives the same state and still reports
+        every section as changed.
+
+        Returns:
+            None on success, or an error message string on failure.
+        """
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
+
+        return self._apply_to_config(profile_name)[1]
+
+    def _persisted_profile_to_restore(self) -> str | None:
+        """Return the persisted profile name, if it still applies to a camera."""
+        persisted = self.load_persisted_profile()
+
+        if not persisted or not any(
+            persisted in cam.profiles for cam in self.config.cameras.values()
+        ):
+            return None
+
+        return persisted
+
+    def restore_persisted_profile_to_config(self) -> None:
+        """Restore the persisted profile into the config, without publishing.
+
+        Called before worker processes start, so they are handed a config that
+        already carries the profile rather than relying on the broadcast that
+        restore_persisted_profile() sends later.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        err = self.apply_profile_to_config(persisted)
+
+        if err:
+            logger.error("Failed to apply persisted profile '%s': %s", persisted, err)
+
+    def restore_persisted_profile(self) -> None:
+        """Re-activate the persisted profile once subscribers are connected.
+
+        The config already carries the profile; this pass publishes it for the
+        processes that start before the config can be corrected, and for the
+        retained MQTT states.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        logger.info("Restoring persisted profile '%s'", persisted)
+        # runtime overrides are layered on top by the dispatcher's replay
+        self.activate_profile(persisted, clear_runtime_overrides=False)
+
     def activate_profile(
         self,
         profile_name: str | None,
@@ -187,23 +276,16 @@ class ProfileManager:
         Returns:
             None on success, or an error message string on failure.
         """
-        if profile_name is not None:
-            if profile_name not in self.config.profiles:
-                return (
-                    f"Profile '{profile_name}' is not defined in the profiles section"
-                )
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
 
         # Track which camera/section pairs get changed for ZMQ publishing
-        changed: dict[str, set[str]] = {}
+        changed, err = self._apply_to_config(profile_name)
 
-        # Reset all cameras to base config
-        self._reset_to_base(changed)
-
-        # Apply new profile overrides if activating
-        if profile_name is not None:
-            err = self._apply_profile_overrides(profile_name, changed)
-            if err:
-                return err
+        if err:
+            return err
 
         # Publish ZMQ updates only for sections that actually changed
         self._publish_updates(changed)

frigate/data_processing/common/license_plate/mixin.py

@@ -72,7 +72,7 @@ class LicensePlateProcessingMixin:
         # Object config
         self.lp_objects: list[str] = []
 
-        for obj, attributes in self.config.model.attributes_map.items():
+        for obj, attributes in self.config.all_attributes_map.items():
             if "license_plate" in attributes:
                 self.lp_objects.append(obj)
 
@@ -1172,6 +1172,28 @@ class LicensePlateProcessingMixin:
 
         return rep["plate"], rep["conf"], rep["char_confidences"], rep["area"]
 
+    def _passes_plate_filters(self, camera: str, plate: str) -> bool:
+        """Check a plate against the configured length and format filters."""
+        if len(plate) < self.lpr_config.min_plate_length:
+            logger.debug(
+                f"{camera}: Filtered out plate '{plate}' due to length ({len(plate)} < {self.lpr_config.min_plate_length})"
+            )
+            return False
+
+        if self.lpr_config.format:
+            try:
+                if not re.fullmatch(self.lpr_config.format, plate):
+                    logger.debug(
+                        f"{camera}: Filtered out plate '{plate}' due to format mismatch"
+                    )
+                    return False
+            except re.error:
+                logger.error(
+                    f"{camera}: Invalid regex in LPR format configuration: {self.lpr_config.format}"
+                )
+
+        return True
+
     def _generate_plate_event(self, camera: str, plate: str, plate_score: float) -> str:
         """Generate a unique ID for a plate event based on camera and text."""
         now = datetime.datetime.now().timestamp()
@@ -1511,10 +1533,14 @@ class LicensePlateProcessingMixin:
             plate_id = None
 
             for existing_id, data in self.detected_license_plates.items():
+                # entries from the object pipeline on this camera have no
+                # last_seen until they pass the filters below
+                last_seen = data.get("last_seen")
+
                 if (
                     data["camera"] == camera
-                    and data["last_seen"] is not None
-                    and current_time - data["last_seen"]
+                    and last_seen is not None
+                    and current_time - last_seen
                     <= self.config.cameras[camera].lpr.expire_time
                 ):
                     similarity = JaroWinkler.similarity(data["plate"], top_plate)
@@ -1525,6 +1551,11 @@ class LicensePlateProcessingMixin:
                         )
                         break
             if plate_id is None:
+                # the event id doubles as the cluster key, so a plate rejected
+                # after this point would leave an entry that never expires
+                if not self._passes_plate_filters(camera, top_plate):
+                    return
+
                 plate_id = self._generate_plate_event(camera, top_plate, avg_confidence)
                 logger.debug(
                     f"{camera}: New plate event for dedicated LPR camera {plate_id}: {top_plate}"
@@ -1569,27 +1600,12 @@ class LicensePlateProcessingMixin:
                 f"{camera}: Clustering changed top plate '{top_plate}' (conf: {avg_confidence:.3f}) to rep '{rep_plate}' (conf: {rep_conf:.3f})"
             )
 
-        # Apply length and format filters to the clustered representative
-        # rather than individual OCR readings, so noisy variants still
-        # contribute to clustering even when they don't pass on their own.
-        if len(rep_plate) < self.lpr_config.min_plate_length:
-            logger.debug(
-                f"{camera}: Filtered out clustered plate '{rep_plate}' due to length ({len(rep_plate)} < {self.lpr_config.min_plate_length})"
-            )
+        # filter the clustered representative rather than individual OCR
+        # readings, so noisy variants still contribute to clustering even
+        # when they don't pass on their own
+        if not self._passes_plate_filters(camera, rep_plate):
             return
 
-        if self.lpr_config.format:
-            try:
-                if not re.fullmatch(self.lpr_config.format, rep_plate):
-                    logger.debug(
-                        f"{camera}: Filtered out clustered plate '{rep_plate}' due to format mismatch"
-                    )
-                    return
-            except re.error:
-                logger.error(
-                    f"{camera}: Invalid regex in LPR format configuration: {self.lpr_config.format}"
-                )
-
         # Update stored rep
         self.detected_license_plates[id].update(
             {

frigate/data_processing/post/object_descriptions.py

@@ -63,8 +63,10 @@ class ObjectDescriptionProcessor(PostProcessorApi):
         """Handle an update to a frame for an object."""
         camera_config = self.config.cameras[camera]
 
-        # no need to save our own thumbnails if genai is not enabled
-        # or if the object has become stationary
+        if not camera_config.objects.genai.enabled:
+            return
+
+        # no need to save our own thumbnails if the object has become stationary
         if not data["stationary"]:
             if data["id"] not in self.tracked_events:
                 self.tracked_events[data["id"]] = []

frigate/data_processing/post/review_descriptions.py

@@ -231,8 +231,8 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     final_data,
                     thumbs,
                     camera_config.review.genai,
-                    list(self.config.model.merged_labelmap.values()),
-                    self.config.model.all_attributes,
+                    sorted(self.config.all_labels),
+                    self.config.all_attributes,
                 ),
             ).start()
 

frigate/data_processing/real_time/face.py

@@ -28,6 +28,7 @@ from frigate.data_processing.common.face.model import (
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed
 from frigate.util.image import area
+from frigate.util.path import safe_join, sanitize_path_component
 
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
@@ -409,9 +410,17 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 )
 
             # write face to library
-            folder = os.path.join(FACE_DIR, label)
+            sanitized_label = sanitize_path_component(label)
+            folder = safe_join(FACE_DIR, label)
+
+            if sanitized_label is None or folder is None:
+                return {
+                    "message": f"Invalid face name: {label}",
+                    "success": False,
+                }
+
             file = os.path.join(
-                folder, f"{label}_{datetime.datetime.now().timestamp()}.webp"
+                folder, f"{sanitized_label}_{datetime.datetime.now().timestamp()}.webp"
             )
             os.makedirs(folder, exist_ok=True)
 

frigate/detectors/detector_config.py

@@ -3,7 +3,7 @@ import json
 import logging
 import os
 from enum import Enum
-from typing import Any
+from typing import Any, ClassVar
 
 import requests
 from pydantic import BaseModel, ConfigDict, Field
@@ -15,6 +15,9 @@ from frigate.util.builtin import generate_color_palette, load_labels
 
 logger = logging.getLogger(__name__)
 
+# attributes that are recognized rather than shown as a logo
+NON_LOGO_ATTRIBUTES = ["face", "license_plate"]
+
 
 class PixelFormatEnum(str, Enum):
     rgb = "rgb"
@@ -44,7 +47,27 @@ class ModelTypeEnum(str, Enum):
     yologeneric = "yolo-generic"
 
 
+class SceneEnum(str, Enum):
+    """The camera environment a detection model is intended for."""
+
+    all = "all"
+    indoor = "indoor"
+    outdoor = "outdoor"
+    indoor_thermal = "indoor_thermal"
+    outdoor_thermal = "outdoor_thermal"
+
+
 class ModelConfig(BaseModel):
+    scene: SceneEnum = Field(
+        default=SceneEnum.all,
+        title="Model scene",
+        description="The camera environment this model is used for. Cameras select a model by setting detect.scene to a matching value, and 'all' is used by any camera that does not set one.",
+    )
+    devices: list[str] = Field(
+        default_factory=list,
+        title="Detection hardware",
+        description="Hardware this model runs on, as '<detector>' or '<detector>:<device>' (for example 'edgetpu:pci:0' or 'openvino:GPU'). Listing the same device more than once runs additional inference processes on it.",
+    )
     path: str | None = Field(
         None,
         title="Custom object detector model path",
@@ -111,7 +134,7 @@ class ModelConfig(BaseModel):
 
     @property
     def non_logo_attributes(self) -> list[str]:
-        return ["face", "license_plate"]
+        return NON_LOGO_ATTRIBUTES
 
     @property
     def all_attributes(self) -> list[str]:
@@ -201,9 +224,7 @@ class ModelConfig(BaseModel):
             unique_attributes.update(attributes)
 
         self._all_attributes = list(unique_attributes)
-        self._all_attribute_logos = list(
-            unique_attributes - set(["face", "license_plate"])
-        )
+        self._all_attribute_logos = list(unique_attributes - set(NON_LOGO_ATTRIBUTES))
 
         self._merged_labelmap = {
             **{int(key): val for key, val in model_info["labelMap"].items()},
@@ -234,6 +255,14 @@ class ModelConfig(BaseModel):
 
 
 class BaseDetectorConfig(BaseModel):
+    # how the trailing part of a device string ("openvino:GPU" -> "GPU") maps onto
+    # this detector's fields, and whether the same device may be listed more than
+    # once to run additional inference processes against it. Most accelerators
+    # multiplex fine, so this is opt-out rather than opt-in.
+    device_spec_field: ClassVar[str] = "device"
+    device_spec_type: ClassVar[type] = str
+    shareable: ClassVar[bool] = True
+
     # the type field must be defined in all subclasses
     type: str = Field(
         default="cpu",

frigate/detectors/detector_types.py

@@ -2,7 +2,7 @@ import importlib
 import logging
 import pkgutil
 from enum import Enum
-from typing import Annotated, Union
+from typing import Annotated, Union, get_args
 
 from pydantic import Field
 
@@ -39,3 +39,21 @@ DetectorConfig = Annotated[
     Union[tuple(BaseDetectorConfig.__subclasses__())],  # noqa: UP007
     Field(discriminator="type"),
 ]
+
+
+def _discriminator_value(config_class: type[BaseDetectorConfig]) -> str | None:
+    """Read the Literal value of a detector config class' type field."""
+    field = config_class.model_fields.get("type")
+
+    if field is None:
+        return None
+
+    values = get_args(field.annotation)
+    return values[0] if values else None
+
+
+config_types: dict[str, type[BaseDetectorConfig]] = {
+    key: config_class
+    for config_class in BaseDetectorConfig.__subclasses__()
+    if (key := _discriminator_value(config_class)) is not None
+}

frigate/detectors/device.py

@@ -0,0 +1,113 @@
+"""Parsing of detection hardware device strings."""
+
+import logging
+from dataclasses import dataclass
+
+from pydantic import TypeAdapter, ValidationError
+
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelConfig
+from frigate.detectors.detector_types import DetectorConfig, config_types
+
+logger = logging.getLogger(__name__)
+
+_detector_adapter: TypeAdapter[BaseDetectorConfig] = TypeAdapter(DetectorConfig)
+
+
+@dataclass(frozen=True)
+class DeviceSpec:
+    """A parsed `<detector>` or `<detector>:<device>` string."""
+
+    raw: str
+    detector: str
+    device: str | None
+
+    @property
+    def shareable(self) -> bool:
+        """Whether this device may be listed more than once."""
+        return config_types[self.detector].shareable
+
+
+class DeviceParseError(ValueError):
+    pass
+
+
+def parse_device(raw: str) -> DeviceSpec:
+    """Parse a device string into its detector type and detector specific device.
+
+    Args:
+        raw: The configured device string, for example 'edgetpu:pci:0'
+
+    Returns:
+        The parsed spec
+
+    Raises:
+        DeviceParseError: If the detector type is unknown or the device is not
+            valid for that detector
+    """
+    detector, separator, device = raw.partition(":")
+
+    if detector not in config_types:
+        raise DeviceParseError(
+            f"'{raw}' does not name a known detector. Available detectors are {', '.join(sorted(config_types))}"
+        )
+
+    spec = DeviceSpec(raw=raw, detector=detector, device=device if separator else None)
+
+    # surface a bad device now rather than when the detection process starts
+    build_detector_config(spec, None)
+    return spec
+
+
+def build_detector_config(
+    spec: DeviceSpec, model: ModelConfig | None
+) -> BaseDetectorConfig:
+    """Build the detector config a device string describes.
+
+    Args:
+        spec: The parsed device spec
+        model: The model this detector runs, if it has been resolved yet
+
+    Returns:
+        The validated detector config
+
+    Raises:
+        DeviceParseError: If the device is not valid for this detector type
+    """
+    config: dict[str, object] = {"type": spec.detector, "model": model}
+
+    if spec.device is not None:
+        config_class = config_types[spec.detector]
+
+        try:
+            config[config_class.device_spec_field] = config_class.device_spec_type(
+                spec.device
+            )
+        except ValueError as err:
+            raise DeviceParseError(
+                f"'{spec.raw}' is not a valid {spec.detector} device: {err}"
+            ) from err
+
+    try:
+        return _detector_adapter.validate_python(config)
+    except ValidationError as err:
+        raise DeviceParseError(f"'{spec.raw}' is not a valid device: {err}") from err
+
+
+def runner_names(devices: list[DeviceSpec]) -> list[str]:
+    """Build a unique name for each device, since a shareable device may repeat.
+
+    Args:
+        devices: Every device spec across every configured model, in config order
+
+    Returns:
+        A name per device, suffixed with '#2', '#3', etc. on repeats
+    """
+    names: list[str] = []
+    seen: dict[str, int] = {}
+
+    for spec in devices:
+        count = seen.get(spec.raw, 0) + 1
+        seen[spec.raw] = count
+        names.append(spec.raw if count == 1 else f"{spec.raw}#{count}")
+
+    return names

frigate/detectors/plugins/cpu_tfl.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Literal
+from typing import ClassVar, Literal
 
 from pydantic import ConfigDict, Field
 
@@ -27,6 +27,9 @@ class CpuDetectorConfig(BaseDetectorConfig):
         title="CPU",
     )
 
+    device_spec_field: ClassVar[str] = "num_threads"
+    device_spec_type: ClassVar[type] = int
+
     type: Literal[DETECTOR_KEY]
     num_threads: int = Field(
         default=3,

frigate/detectors/plugins/degirum.py

@@ -1,157 +0,0 @@
-import logging
-import queue
-from typing import Literal
-
-import numpy as np
-from pydantic import ConfigDict, Field
-
-from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
-
-logger = logging.getLogger(__name__)
-DETECTOR_KEY = "degirum"
-
-
-### DETECTOR CONFIG ###
-class DGDetectorConfig(BaseDetectorConfig):
-    """DeGirum detector for running models via DeGirum cloud or local inference services."""
-
-    model_config = ConfigDict(
-        title="DeGirum",
-    )
-
-    type: Literal[DETECTOR_KEY]
-    location: str = Field(
-        default=None,
-        title="Inference Location",
-        description="Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1').",
-    )
-    zoo: str = Field(
-        default=None,
-        title="Model Zoo",
-        description="Path or URL to the DeGirum model zoo.",
-    )
-    token: str = Field(
-        default=None,
-        title="DeGirum Cloud Token",
-        description="Token for DeGirum Cloud access.",
-    )
-
-
-### ACTUAL DETECTOR  ###
-class DGDetector(DetectionApi):
-    type_key = DETECTOR_KEY
-
-    def __init__(self, detector_config: DGDetectorConfig):
-        try:
-            import degirum as dg
-        except ModuleNotFoundError:
-            raise ImportError("Unable to import DeGirum detector.") from None
-
-        self._queue = queue.Queue()
-        self._zoo = dg.connect(
-            detector_config.location, detector_config.zoo, detector_config.token
-        )
-
-        logger.debug(f"Models in zoo: {self._zoo.list_models()}")
-
-        self.dg_model = self._zoo.load_model(
-            detector_config.model.path,
-        )
-
-        # Setting input image format to raw reduces preprocessing time
-        self.dg_model.input_image_format = "RAW"
-
-        # Prioritize the most powerful hardware available
-        self.select_best_device_type()
-        # Frigate handles pre processing as long as these are all set
-        input_shape = self.dg_model.input_shape[0]
-        self.model_height = input_shape[1]
-        self.model_width = input_shape[2]
-
-        # Passing in dummy frame so initial connection latency happens in
-        # init function and not during actual prediction
-        frame = np.zeros(
-            (detector_config.model.width, detector_config.model.height, 3),
-            dtype=np.uint8,
-        )
-        # Pass in frame to overcome first frame latency
-        self.dg_model(frame)
-        self.prediction = self.prediction_generator()
-
-    def select_best_device_type(self):
-        """
-        Helper function that selects fastest hardware available per model runtime
-        """
-        types = self.dg_model.supported_device_types
-
-        device_map = {
-            "OPENVINO": ["GPU", "NPU", "CPU"],
-            "HAILORT": ["HAILO8L", "HAILO8"],
-            "N2X": ["ORCA1", "CPU"],
-            "ONNX": ["VITIS_NPU", "CPU"],
-            "RKNN": ["RK3566", "RK3568", "RK3588"],
-            "TENSORRT": ["DLA", "GPU", "DLA_ONLY"],
-            "TFLITE": ["ARMNN", "EDGETPU", "CPU"],
-        }
-
-        runtime = types[0].split("/")[0]
-        # Just create an array of format {runtime}/{hardware} for every hardware
-        # in the value for appropriate key in device_map
-        self.dg_model.device_type = [
-            f"{runtime}/{hardware}" for hardware in device_map[runtime]
-        ]
-
-    def prediction_generator(self):
-        """
-        Generator for all incoming frames. By using this generator, we don't have to keep
-        reconnecting our websocket on every "predict" call.
-        """
-        logger.debug("Prediction generator was called")
-        with self.dg_model as model:
-            while 1:
-                logger.info(f"q size before calling get: {self._queue.qsize()}")
-                data = self._queue.get(block=True)
-                logger.info(f"q size after calling get: {self._queue.qsize()}")
-                logger.debug(
-                    f"Data we're passing into model predict: {data}, shape of data: {data.shape}"
-                )
-                result = model.predict(data)
-                logger.debug(f"Prediction result: {result}")
-                yield result
-
-    def detect_raw(self, tensor_input):
-        # Reshaping tensor to work with pysdk
-        truncated_input = tensor_input.reshape(tensor_input.shape[1:])
-        logger.debug(f"Detect raw was called for tensor input: {tensor_input}")
-
-        # add tensor_input to input queue
-        self._queue.put(truncated_input)
-        logger.debug(f"Queue size after adding truncated input: {self._queue.qsize()}")
-
-        # define empty detection result
-        detections = np.zeros((20, 6), np.float32)
-        # grab prediction
-        res = next(self.prediction)
-
-        # If we have an empty prediction, return immediately
-        if len(res.results) == 0 or len(res.results[0]) == 0:
-            return detections
-
-        i = 0
-        for result in res.results:
-            if i >= 20:
-                break
-
-            detections[i] = [
-                result["category_id"],
-                float(result["score"]),
-                result["bbox"][1] / self.model_height,
-                result["bbox"][0] / self.model_width,
-                result["bbox"][3] / self.model_height,
-                result["bbox"][2] / self.model_width,
-            ]
-            i += 1
-
-        logger.debug(f"Detections output: {detections}")
-        return detections

frigate/detectors/plugins/edgetpu_tfl.py

@@ -1,7 +1,7 @@
 import logging
 import math
 import os
-from typing import Literal
+from typing import ClassVar, Literal
 
 import cv2
 import numpy as np
@@ -9,6 +9,7 @@ from pydantic import ConfigDict, Field
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
+from frigate.util.model import xyxy_to_xywh_for_nms
 
 try:
     from tflite_runtime.interpreter import Interpreter, load_delegate
@@ -27,6 +28,9 @@ class EdgeTpuDetectorConfig(BaseDetectorConfig):
         title="EdgeTPU",
     )
 
+    # a TPU can only be opened by one process
+    shareable: ClassVar[bool] = False
+
     type: Literal[DETECTOR_KEY]
     device: str = Field(
         default=None,
@@ -297,7 +301,7 @@ class EdgeTpuTfl(DetectionApi):
             # until after filtering out redundant boxes
             # Shift the logit scores to be non-negative (required by cv2)
             indices = cv2.dnn.NMSBoxes(
-                bboxes=boxes_filtered_decoded,
+                bboxes=xyxy_to_xywh_for_nms(boxes_filtered_decoded),
                 scores=max_scores_filtered_shiftedpositive,
                 score_threshold=(
                     self.min_logit_value + self.logit_shift_to_positive_values

frigate/detectors/plugins/memryx.py

@@ -5,7 +5,7 @@ import shutil
 import urllib.request
 import zipfile
 from queue import Queue
-from typing import Literal
+from typing import ClassVar, Literal
 
 import cv2
 import numpy as np
@@ -17,6 +17,7 @@ from frigate.detectors.detector_config import (
     ModelTypeEnum,
 )
 from frigate.util.file import FileLock
+from frigate.util.model import xyxy_to_xywh_for_nms
 
 logger = logging.getLogger(__name__)
 
@@ -36,6 +37,9 @@ class MemryXDetectorConfig(BaseDetectorConfig):
         title="MemryX",
     )
 
+    # an accelerator can only be opened by one process
+    shareable: ClassVar[bool] = False
+
     type: Literal[DETECTOR_KEY]
     device: str = Field(
         default="PCIe",
@@ -581,7 +585,7 @@ class MemryXDetector(DetectionApi):
             # Convert coordinates to integers
             x_min, y_min, x_max, y_max = map(int, [x_min, y_min, x_max, y_max])
 
-            # Append valid detections [class_id, confidence, x, y, width, height]
+            # Append valid detections [class_id, confidence, x_min, y_min, x_max, y_max]
             detections.append([class_id, confidence, x_min, y_min, x_max, y_max])
 
         final_detections = np.zeros((20, 6), np.float32)
@@ -595,7 +599,7 @@ class MemryXDetector(DetectionApi):
         detections = np.array(detections, dtype=np.float32)
 
         # Apply Non-Maximum Suppression (NMS)
-        bboxes = detections[:, 2:6].tolist()  # (x_min, y_min, width, height)
+        bboxes = xyxy_to_xywh_for_nms(detections[:, 2:6])
         scores = detections[:, 1].tolist()  # Confidence scores
 
         indices = cv2.dnn.NMSBoxes(bboxes, scores, 0.45, 0.5)

frigate/detectors/plugins/openvino.py

@@ -28,7 +28,7 @@ class OvDetectorConfig(BaseDetectorConfig):
 
     type: Literal[DETECTOR_KEY]
     device: str = Field(
-        default=None,
+        default="AUTO",
         title="Device Type",
         description="The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU').",
     )
@@ -226,12 +226,12 @@ class OvDetector(DetectionApi):
 
             conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
             # Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
-            detections = np.concatenate(
+            predictions = np.concatenate(
                 (image_pred[:, :5], class_conf, class_pred), axis=1
             )
-            detections = detections[conf_mask]
+            predictions = predictions[conf_mask]
 
-            ordered = detections[detections[:, 5].argsort()[::-1]][:20]
+            ordered = predictions[predictions[:, 5].argsort()[::-1]][:20]
 
             for i, object_detected in enumerate(ordered):
                 detections[i] = self.process_yolo(

frigate/detectors/plugins/rknn.py

@@ -2,7 +2,7 @@ import logging
 import os.path
 import re
 import urllib.request
-from typing import Literal
+from typing import ClassVar, Literal
 
 import cv2
 import numpy as np
@@ -12,7 +12,7 @@ from frigate.const import MODEL_CACHE_DIR, SUPPORTED_RK_SOCS
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import RKNNModelRunner
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
-from frigate.util.model import post_process_yolo
+from frigate.util.model import post_process_yolo, xyxy_to_xywh_for_nms
 from frigate.util.rknn_converter import auto_convert_model
 
 logger = logging.getLogger(__name__)
@@ -35,6 +35,9 @@ class RknnDetectorConfig(BaseDetectorConfig):
         title="RKNN",
     )
 
+    device_spec_field: ClassVar[str] = "num_cores"
+    device_spec_type: ClassVar[type] = int
+
     type: Literal[DETECTOR_KEY]
     num_cores: int = Field(
         default=0,
@@ -285,7 +288,7 @@ class Rknn(DetectionApi):
 
         # run nms
         indices = cv2.dnn.NMSBoxes(
-            bboxes=boxes,
+            bboxes=xyxy_to_xywh_for_nms(boxes),
             scores=scores,
             score_threshold=0.4,
             nms_threshold=0.4,

frigate/detectors/plugins/tensorrt.py

@@ -14,7 +14,7 @@ try:
 except ModuleNotFoundError:
     TRT_SUPPORT = False
 
-from typing import Literal
+from typing import ClassVar, Literal
 
 from pydantic import ConfigDict, Field
 
@@ -53,6 +53,8 @@ class TensorRTDetectorConfig(BaseDetectorConfig):
         title="TensorRT",
     )
 
+    device_spec_type: ClassVar[type] = int
+
     type: Literal[DETECTOR_KEY]
     device: int = Field(
         default=0, title="GPU Device Index", description="The GPU device index to use."

frigate/embeddings/__init__.py

@@ -21,6 +21,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event
 from frigate.util.builtin import serialize
 from frigate.util.classification import kickoff_model_training
+from frigate.util.path import safe_join
 from frigate.util.process import FrigateProcess
 
 from .maintainer import EmbeddingMaintainer
@@ -33,7 +34,7 @@ class EmbeddingProcess(FrigateProcess):
     def __init__(
         self,
         config: FrigateConfig,
-        metrics: DataProcessorMetrics | None,
+        metrics: DataProcessorMetrics,
         stop_event: MpEvent,
     ) -> None:
         super().__init__(
@@ -234,11 +235,16 @@ class EmbeddingsContext:
         )
 
     def delete_face_ids(self, face: str, ids: list[str]) -> None:
-        folder = os.path.join(FACE_DIR, face)
-        for id in ids:
-            file_path = os.path.join(folder, id)
+        folder = safe_join(FACE_DIR, face)
 
-            if os.path.isfile(file_path):
+        if folder is None:
+            logger.warning("Not deleting faces for invalid name %s", face)
+            return
+
+        for id in ids:
+            file_path = safe_join(folder, id)
+
+            if file_path and os.path.isfile(file_path):
                 os.unlink(file_path)
 
         if face != "train" and len(os.listdir(folder)) == 0:

frigate/embeddings/maintainer.py

@@ -78,6 +78,16 @@ logger = logging.getLogger(__name__)
 
 MAX_THUMBNAILS = 10
 
+GENAI_UPDATE_TOPICS = frozenset(
+    {
+        CameraConfigUpdateEnum.add.name,
+        CameraConfigUpdateEnum.objects.name,
+        CameraConfigUpdateEnum.object_genai.name,
+        CameraConfigUpdateEnum.review.name,
+        CameraConfigUpdateEnum.review_genai.name,
+    }
+)
+
 
 class EmbeddingMaintainer(threading.Thread):
     """Handle embedding queue and post event updates."""
@@ -85,7 +95,7 @@ class EmbeddingMaintainer(threading.Thread):
     def __init__(
         self,
         config: FrigateConfig,
-        metrics: DataProcessorMetrics | None,
+        metrics: DataProcessorMetrics,
         stop_event: MpEvent,
     ) -> None:
         super().__init__(name="embeddings_maintainer")
@@ -220,16 +230,6 @@ class EmbeddingMaintainer(threading.Thread):
         # post processors
         self.post_processors: list[PostProcessorApi] = []
 
-        if any(c.review.genai.enabled_in_config for c in self.config.cameras.values()):
-            self.post_processors.append(
-                ReviewDescriptionProcessor(
-                    self.config,
-                    self.requestor,
-                    self.metrics,
-                    self.genai_manager,
-                )
-            )
-
         if self.config.lpr.enabled:
             self.post_processors.append(
                 LicensePlatePostProcessor(
@@ -252,9 +252,9 @@ class EmbeddingMaintainer(threading.Thread):
                 )
             )
 
-        semantic_trigger_processor: SemanticTriggerProcessor | None = None
+        self.semantic_trigger_processor: SemanticTriggerProcessor | None = None
         if self.config.semantic_search.enabled:
-            semantic_trigger_processor = SemanticTriggerProcessor(
+            self.semantic_trigger_processor = SemanticTriggerProcessor(
                 db,
                 self.config,
                 self.requestor,
@@ -262,9 +262,49 @@ class EmbeddingMaintainer(threading.Thread):
                 metrics,
                 self.embeddings,
             )
-            self.post_processors.append(semantic_trigger_processor)
+            self.post_processors.append(self.semantic_trigger_processor)
 
-        if any(c.objects.genai.enabled_in_config for c in self.config.cameras.values()):
+        self._sync_genai_processors()
+
+        self.stop_event = stop_event
+
+        # recordings data
+        self.recordings_available_through: dict[str, float] = {}
+
+    def _sync_genai_processors(self) -> None:
+        """Create GenAI post processors for cameras that have GenAI enabled.
+
+        Called at startup and again after camera config updates so enabling
+        GenAI on the first camera does not require a restart. Processors are
+        never removed once created.
+
+        A profile can turn GenAI on without setting enabled_in_config, so both
+        flags are checked.
+        """
+        cameras = self.config.cameras.values()
+
+        if any(
+            c.review.genai.enabled or c.review.genai.enabled_in_config for c in cameras
+        ) and not any(
+            isinstance(p, ReviewDescriptionProcessor) for p in self.post_processors
+        ):
+            logger.debug("Initializing review description processor")
+            self.post_processors.append(
+                ReviewDescriptionProcessor(
+                    self.config,
+                    self.requestor,
+                    self.metrics,
+                    self.genai_manager,
+                )
+            )
+
+        if any(
+            c.objects.genai.enabled or c.objects.genai.enabled_in_config
+            for c in cameras
+        ) and not any(
+            isinstance(p, ObjectDescriptionProcessor) for p in self.post_processors
+        ):
+            logger.debug("Initializing object description processor")
             self.post_processors.append(
                 ObjectDescriptionProcessor(
                     self.config,
@@ -272,19 +312,21 @@ class EmbeddingMaintainer(threading.Thread):
                     self.requestor,
                     self.metrics,
                     self.genai_manager,
-                    semantic_trigger_processor,
+                    self.semantic_trigger_processor,
                 )
             )
 
-        self.stop_event = stop_event
+    def _check_camera_config_updates(self) -> None:
+        """Apply camera config updates and register newly enabled processors."""
+        updated_topics = self.config_updater.check_for_updates()
 
-        # recordings data
-        self.recordings_available_through: dict[str, float] = {}
+        if updated_topics.keys() & GENAI_UPDATE_TOPICS:
+            self._sync_genai_processors()
 
     def run(self) -> None:
         """Maintain a SQLite-vec database for semantic search."""
         while not self.stop_event.is_set():
-            self.config_updater.check_for_updates()
+            self._check_camera_config_updates()
             self._check_enrichment_config_updates()
             self._process_requests()
             self._process_updates()

frigate/events/maintainer.py

@@ -159,7 +159,8 @@ class EventProcessor(threading.Thread):
             if width is None or height is None:
                 return
 
-            first_detector = list(self.config.detectors.values())[0]
+            camera_model = self.config.model_for_camera(camera)
+            camera_detector = self.config.devices_for_model(camera_model)[0].detector
 
             start_time = event_data["start_time"]
             end_time = (
@@ -229,13 +230,9 @@ class EventProcessor(threading.Thread):
                 Event.thumbnail: event_data.get("thumbnail"),
                 Event.has_clip: event_data["has_clip"],
                 Event.has_snapshot: event_data["has_snapshot"],
-                Event.model_hash: first_detector.model.model_hash
-                if first_detector.model
-                else None,
-                Event.model_type: first_detector.model.model_type
-                if first_detector.model
-                else None,
-                Event.detector_type: first_detector.type,
+                Event.model_hash: camera_model.model_hash,
+                Event.model_type: camera_model.model_type,
+                Event.detector_type: camera_detector,
                 Event.data: {
                     "box": box,
                     "region": region,

frigate/genai/__init__.py

@@ -23,6 +23,7 @@ from frigate.genai.prompts import (
     build_review_summary_prompt,
 )
 from frigate.models import Event
+from frigate.util.builtin import has_non_finite_number
 
 logger = logging.getLogger(__name__)
 
@@ -164,6 +165,15 @@ class GenAIClient:
                 except json.JSONDecodeError as je:
                     logger.error("Failed to parse review description JSON: %s", je)
                     return None
+
+                # model_construct skips validation, so non-finite numbers that
+                # the validated path would have rejected have to be caught here
+                if has_non_finite_number(raw):
+                    logger.error(
+                        "Discarding review description containing non-finite numbers."
+                    )
+                    return None
+
                 # observations and confidence are required on the model; fill an empty default
                 # if the response omitted it so attribute access stays safe.
                 raw.setdefault("observations", [])

frigate/genai/prompts.py

@@ -262,6 +262,10 @@ def get_tool_definitions(
     `attribute` parameter is exposed for filtering by their labels. When the
     embeddings model only understands English (JinaV1), the `semantic_query`
     description instructs the model to write the query in English.
+
+    Descriptions here stay mechanical: which tool to reach for, and how the
+    filters relate to each other, is stated once in the system prompt so the
+    guidance is not paid for twice on every request.
     """
     search_objects_properties: dict[str, Any] = {
         "camera": {
@@ -270,26 +274,13 @@ def get_tool_definitions(
         },
         "label": {
             "type": "string",
-            "description": (
-                "Generic object class to filter by — one of the tracked detector "
-                "labels such as 'person', 'package', 'car', 'dog', 'bird'. Use "
-                "this for broad queries like 'show me all cars today'. Combine "
-                "with semantic_query when the user also describes appearance or "
-                "behavior (e.g. label='person', semantic_query='riding a lawn "
-                "mower')."
-            ),
+            "description": "Tracked object class to filter by.",
         },
         "sub_label": {
             "type": "string",
             "description": (
-                "Filter by a DISCRETE NAMED entity recognized in the detection. "
-                "Use this for: a known person's name ('John'), a delivery "
-                "company ('Amazon', 'UPS'), a recognized animal species or "
-                "breed ('blue jay', 'cardinal', 'golden retriever'), or a "
-                "license plate string. When filtering by a specific name, set "
-                "only sub_label and leave label unset. Do NOT use sub_label "
-                "for descriptions of appearance, clothing, or actions — those "
-                "belong in semantic_query."
+                "Name recognized in the detection: a person, delivery company, "
+                "animal species or breed, or license plate."
             ),
         },
         "after": {
@@ -313,20 +304,11 @@ def get_tool_definitions(
     }
 
     if attribute_classifications:
-        model_outline = "; ".join(
-            f"{m['name']} (applies to {', '.join(m['objects']) or 'any object'})"
-            for m in attribute_classifications
-        )
         search_objects_properties["attribute"] = {
             "type": "string",
             "description": (
-                "Filter by a classification attribute label produced by a "
-                "configured attribute classification model. Use this INSTEAD "
-                "of semantic_query when the user's request matches one of "
-                "these classifications. Configured models: "
-                f"{model_outline}. "
-                "Set the value to the attribute label that matches the user's "
-                "phrasing (case-sensitive)."
+                "Attribute label produced by a configured classification model "
+                "(case-sensitive)."
             ),
         }
 
@@ -334,29 +316,12 @@ def get_tool_definitions(
         search_objects_properties["semantic_query"] = {
             "type": "string",
             "description": (
-                "Optional natural-language description of a PHYSICAL "
-                "CHARACTERISTIC, APPEARANCE, or ACTIVITY the user mentioned, "
-                "used to semantically narrow results. Only set this when the "
-                "user describes something beyond what label and sub_label can "
-                "express on their own.\n"
-                "USE for descriptive phrases like: 'riding a lawn mower', "
-                "'wearing a red jacket', 'carrying a package', 'walking a "
-                "dog', 'on a bicycle', 'holding an umbrella'.\n"
-                "DO NOT USE for:\n"
-                "- specific named people, pets, or delivery companies → use sub_label\n"
-                "- animal species or breed names like 'blue jay', 'cardinal', "
-                "'golden retriever' → use sub_label\n"
-                "- license plate strings → use sub_label\n"
-                "- generic object queries like 'all cars today' or 'every "
-                "person' → use label alone with no semantic_query\n"
-                "When set, combine with label/time/camera/zone filters as "
-                "usual (e.g. label='person', semantic_query='riding a lawn "
-                "mower', after='2024-05-01T00:00:00Z')."
+                "Description of an appearance or activity, used to semantically "
+                "narrow results."
                 + (
-                    " The configured embeddings model only understands "
-                    "English, so always write semantic_query in English, "
-                    "translating the user's description if they phrased it "
-                    "in another language."
+                    " The configured embeddings model only understands English, so "
+                    "always write this in English, translating the user's "
+                    "description if they phrased it in another language."
                     if embeddings_language == "english"
                     else ""
                 )
@@ -364,26 +329,10 @@ def get_tool_definitions(
         }
 
     search_objects_description = (
-        "Search the historical record of detected objects in Frigate. "
-        "Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
-        "'when was the last car?', 'show me detections from yesterday'. "
-        "Do NOT use this for monitoring or alerting requests about future events — "
-        "use start_camera_watch instead for those. "
-        "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car).\n\n"
-        "Choose filters based on what the user is asking for:\n"
-        "- Generic class query ('show me all cars today'): set `label` only.\n"
-        "- Specific NAMED entity (known person, delivery company, animal "
-        "species/breed like 'blue jay' or 'golden retriever', license "
-        "plate): set `sub_label` only and leave `label` unset.\n"
+        "Search the historical record of tracked detections. Use this ONLY for "
+        "questions about the PAST, e.g. 'did anyone come by today?', 'when was the "
+        "last car?'. For alerting on future events use start_camera_watch instead."
     )
-    if semantic_search_enabled:
-        search_objects_description += (
-            "- Physical CHARACTERISTIC, APPEARANCE, or ACTIVITY that is not a "
-            "discrete name ('person riding a lawn mower', 'someone in a red "
-            "jacket', 'person carrying a package'): set `semantic_query` with "
-            "the descriptive phrase, optionally alongside `label` for the "
-            "object class. Do NOT put descriptive phrases in sub_label."
-        )
 
     return [
         {
@@ -398,20 +347,30 @@ def get_tool_definitions(
                 "required": [],
             },
         },
+        {
+            "type": "function",
+            "function": {
+                "name": "get_categorized_object_names",
+                "description": (
+                    "Every name that can be attached as a sub_label, grouped by object "
+                    "type: recognized faces, named license plates, classification "
+                    "categories, and delivery logos. Takes no arguments and always "
+                    "returns the complete map."
+                ),
+                "parameters": {
+                    "type": "object",
+                    "properties": {},
+                    "required": [],
+                },
+            },
+        },
         {
             "type": "function",
             "function": {
                 "name": "find_similar_objects",
                 "description": (
-                    "Find tracked objects that are visually and semantically similar "
-                    "to a specific past event. Use this when the user references a "
-                    "particular object they have seen and wants to find other "
-                    "sightings of the same or similar one ('that green car', 'the "
-                    "person in the red jacket', 'the package that was delivered'). "
-                    "Prefer this over search_objects whenever the user's intent is "
-                    "'find more like this specific one.' Use search_objects first "
-                    "only if you need to locate the anchor event. Requires semantic "
-                    "search to be enabled."
+                    "Find tracked objects visually and semantically similar to a "
+                    "specific past event. Requires semantic search to be enabled."
                 ),
                 "parameters": {
                     "type": "object",
@@ -473,9 +432,8 @@ def get_tool_definitions(
             "function": {
                 "name": "set_camera_state",
                 "description": (
-                    "Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
-                    "Use camera='*' to apply to all cameras at once. "
-                    "Only call this tool when the user explicitly asks to change a camera setting. "
+                    "Change a camera's feature state, e.g. turn detection on or off. "
+                    "Only call this when the user explicitly asks to change a setting. "
                     "Requires admin privileges."
                 ),
                 "parameters": {
@@ -510,14 +468,14 @@ def get_tool_definitions(
                             ],
                             "description": (
                                 "The feature to change. Most features accept ON or OFF. "
-                                "birdseye_mode accepts CONTINUOUS, MOTION, or OBJECTS. "
+                                "birdseye_mode accepts CONTINUOUS, MOTION, OBJECTS, STATIONARY_OBJECTS, or a comma-separated combination. "
                                 "motion_contour_area and motion_threshold accept a number. "
                                 "profile accepts a profile name or 'none' to deactivate (requires camera='*')."
                             ),
                         },
                         "value": {
                             "type": "string",
-                            "description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
+                            "description": "The value to set, as accepted by the chosen feature.",
                         },
                     },
                     "required": ["camera", "feature", "value"],
@@ -529,11 +487,9 @@ def get_tool_definitions(
             "function": {
                 "name": "get_live_context",
                 "description": (
-                    "Get the current live image and detection information for a single camera: objects being tracked, "
-                    "zones, timestamps. Use this to understand what is visible in the live view. "
-                    "Call this when answering questions about what is happening right now on a specific camera. "
-                    "Operates on one camera at a time; call the tool again for each additional camera. "
-                    "Wildcards and empty values are not accepted."
+                    "Current live image and detections (tracked objects, zones, "
+                    "timestamps) for one camera. Use this for questions about what is "
+                    "happening right now. Call it again for each additional camera."
                 ),
                 "parameters": {
                     "type": "object",
@@ -541,8 +497,8 @@ def get_tool_definitions(
                         "camera": {
                             "type": "string",
                             "description": (
-                                "Exact name of a single camera to get live context for. "
-                                "Wildcards (e.g. '*', 'all') and empty strings are not accepted."
+                                "Exact name of a single camera. Wildcards (e.g. '*', "
+                                "'all') and empty strings are not accepted."
                             ),
                         },
                     },
@@ -555,10 +511,9 @@ def get_tool_definitions(
             "function": {
                 "name": "start_camera_watch",
                 "description": (
-                    "Start a continuous VLM watch job that monitors a camera and sends a notification "
-                    "when a specified condition is met. Use this when the user wants to be alerted about "
-                    "a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
-                    "Only one watch job can run at a time. Returns a job ID."
+                    "Start a continuous watch job that monitors a camera and notifies "
+                    "the user when a condition is met, e.g. 'tell me when guests "
+                    "arrive'. Only one watch job can run at a time. Returns a job ID."
                 ),
                 "parameters": {
                     "type": "object",
@@ -598,10 +553,7 @@ def get_tool_definitions(
             "type": "function",
             "function": {
                 "name": "stop_camera_watch",
-                "description": (
-                    "Cancel the currently running VLM watch job. Use this when the user wants to "
-                    "stop a previously started watch, e.g. 'stop watching the front door'."
-                ),
+                "description": "Cancel the currently running watch job.",
                 "parameters": {
                     "type": "object",
                     "properties": {},
@@ -614,11 +566,9 @@ def get_tool_definitions(
             "function": {
                 "name": "get_profile_status",
                 "description": (
-                    "Get the current profile status including the active profile and "
-                    "timestamps of when each profile was last activated. Use this to "
-                    "determine time periods for recap requests — e.g. when the user asks "
-                    "'what happened while I was away?', call this first to find the relevant "
-                    "time window based on profile activation history."
+                    "Get the active profile and when each profile was last activated. "
+                    "Call this before get_recap to derive the time window for requests "
+                    "like 'what happened while I was away?'."
                 ),
                 "parameters": {
                     "type": "object",
@@ -632,11 +582,9 @@ def get_tool_definitions(
             "function": {
                 "name": "get_recap",
                 "description": (
-                    "Get a recap of all activity (alerts and detections) for a given time period. "
-                    "Use this after calling get_profile_status to retrieve what happened during "
-                    "a specific window — e.g. 'what happened while I was away?'. Returns a "
-                    "chronological list of activity with camera, objects, zones, and GenAI-generated "
-                    "descriptions when available. Summarize the results for the user."
+                    "Get all activity (alerts and detections) for a time period, as a "
+                    "chronological list with camera, objects, zones, and descriptions "
+                    "when available. Summarize the results for the user."
                 ),
                 "parameters": {
                     "type": "object",
@@ -723,14 +671,13 @@ def build_chat_system_prompt(
         )
         speed_units_section = f"\n\nReport object speeds to the user in {speed_unit}."
 
-    semantic_search_section = ""
+    filter_routing_section = (
+        "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
+        "- Generic class ('show me all cars today'): set `label` only.\n"
+        "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset. Call get_categorized_object_names first and use the exact spelling it returns; a guessed spelling matches nothing. If the name is absent, say it is not configured rather than searching for it."
+    )
     if semantic_search_enabled:
-        semantic_search_section = (
-            "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
-            "- Generic class ('show me all cars today'): set `label` only.\n"
-            "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'cardinal', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset.\n"
-            "- Physical characteristic, appearance, or activity that is NOT a discrete name ('find me people riding a lawn mower', 'someone in a red jacket', 'a person carrying a package'): set `semantic_query` with the descriptive phrase, optionally combined with `label` for the object class. Never put descriptive phrases in `sub_label`."
-        )
+        filter_routing_section += "\n- Physical characteristic, appearance, or activity that is NOT a discrete name ('riding a lawn mower', 'someone in a red jacket'): set `semantic_query` with the descriptive phrase, optionally combined with `label`. Never put descriptive phrases in `sub_label`."
 
     attribute_classification_section = ""
     if attribute_classifications:
@@ -739,9 +686,9 @@ def build_chat_system_prompt(
             for m in attribute_classifications
         )
         attribute_classification_section = (
-            "\n\nAttribute classification models are configured for the following object types:\n"
+            "\n\nConfigured attribute classification models:\n"
             f"{model_lines}\n"
-            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases that fall outside the configured attribute labels."
+            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label (case-sensitive) rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases outside the configured attribute labels."
         )
 
     return f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
@@ -750,9 +697,6 @@ Current server local date and time: {current_date_str} at {current_time_str}
 
 Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.
 
-Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
-When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
-When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
-Always be accurate with time calculations based on the current date provided.
+Always present times in the server's local timezone. When tool results include start_time_local and end_time_local, quote those strings exactly; never convert or invent timestamps, and fall back to UTC or ISO format only when a result has no local time fields. Resolve relative dates like "today" or "this week" against the current date above, and pass dates to tools in ISO 8601 (e.g. {current_date_str}T00:00:00Z for the start of today).
 
-When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{semantic_search_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
+When the user refers to a specific object they have seen ("that green car", "the person in the red jacket", "a package left today"), prefer find_similar_objects over search_objects, using search_objects only to locate the anchor event and passing its id along. Keep search_objects for generic queries like "show me all cars today". If a user message begins with [attached_event:<id>], treat that id as the anchor for any similarity or "tell me more" request in the same message.{filter_routing_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""

frigate/object_detection/util.py

@@ -5,7 +5,19 @@ import threading
 
 from numpy import ndarray
 
-from frigate.detectors.detector_config import InputTensorEnum
+from frigate.detectors.detector_config import InputTensorEnum, ModelConfig
+
+
+def detection_frame_size(model: ModelConfig) -> int:
+    """Get the shared memory size a camera needs to hand frames to a model.
+
+    Args:
+        model: The model the camera runs on
+
+    Returns:
+        Size in bytes of one model input frame
+    """
+    return model.height * model.width * 3
 
 
 class RequestStore:

frigate/output/birdseye.py

@@ -9,6 +9,7 @@ import queue
 import subprocess as sp
 import threading
 import traceback
+from dataclasses import dataclass
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
 
@@ -16,7 +17,7 @@ import cv2
 import numpy as np
 
 from frigate.comms.inter_process import InterProcessRequestor
-from frigate.config import BirdseyeModeEnum, FfmpegConfig, FrigateConfig
+from frigate.config import BirdseyeModeConfig, FfmpegConfig, FrigateConfig
 from frigate.const import BASE_DIR, BIRDSEYE_PIPE, INSTALL_DIR, UPDATE_BIRDSEYE_LAYOUT
 from frigate.output.ws_auth import ws_has_camera_access
 from frigate.util.image import (
@@ -28,6 +29,15 @@ from frigate.util.image import (
 logger = logging.getLogger(__name__)
 
 
+@dataclass(frozen=True, slots=True)
+class BirdseyeActivity:
+    """Activity signals used to decide whether a camera is shown in Birdseye."""
+
+    has_active_object: bool
+    has_stationary_object: bool
+    has_motion: bool
+
+
 def get_standard_aspect_ratio(width: int, height: int) -> tuple[int, int]:
     """Ensure that only standard aspect ratios are used."""
     # it is important that all ratios have the same scale
@@ -409,18 +419,16 @@ class BirdsEyeFrameManager:
         )
 
     def camera_active(
-        self, mode: Any, object_box_count: int, motion_box_count: int
+        self,
+        mode: BirdseyeModeConfig,
+        activity: BirdseyeActivity,
     ) -> bool:
-        if mode == BirdseyeModeEnum.continuous:
-            return True
-
-        if mode == BirdseyeModeEnum.motion and motion_box_count > 0:
-            return True
-
-        if mode == BirdseyeModeEnum.objects and object_box_count > 0:
-            return True
-
-        return False
+        return (
+            mode.continuous
+            or (mode.motion and activity.has_motion)
+            or (mode.objects and activity.has_active_object)
+            or (mode.stationary_objects and activity.has_stationary_object)
+        )
 
     def get_camera_coordinates(self) -> dict[str, dict[str, int]]:
         """Return the coordinates of each camera in the current layout."""
@@ -604,112 +612,92 @@ class BirdsEyeFrameManager:
     ) -> list[list[Any]] | None:
         """Calculate the optimal layout for 2+ cameras."""
 
-        def map_layout(
-            camera_layout: list[list[Any]], row_height: int
-        ) -> tuple[int, int, list[list[Any]] | None]:
-            """Map the calculated layout."""
-            candidate_layout = []
-            starting_x = 0
-            x = 0
-            max_width = 0
-            y = 0
+        def find_available_x(
+            current_x: int,
+            width: int,
+            reserved_ranges: list[tuple[int, int]],
+            max_width: int,
+        ) -> int | None:
+            """Find the first horizontal slot that does not collide with reservations."""
+            x = current_x
 
-            for row in camera_layout:
-                final_row = []
-                max_width = max(max_width, x)
-                x = starting_x
-                for cameras in row:
-                    camera_dims = self.cameras[cameras[0]]["dimensions"].copy()
-                    camera_aspect = cameras[1]
+            for reserved_start, reserved_end in sorted(reserved_ranges):
+                if x >= reserved_end:
+                    continue
 
-                    if camera_dims[1] > camera_dims[0]:
-                        scaled_height = int(row_height * 2)
-                        scaled_width = int(scaled_height * camera_aspect)
-                        starting_x = scaled_width
-                    else:
-                        scaled_height = row_height
-                        scaled_width = int(scaled_height * camera_aspect)
+                if x + width <= reserved_start:
+                    return x
 
-                    # layout is too large
-                    if (
-                        x + scaled_width > self.canvas.width
-                        or y + scaled_height > self.canvas.height
-                    ):
-                        return x + scaled_width, y + scaled_height, None
+                x = max(x, reserved_end)
 
-                    final_row.append((cameras[0], (x, y, scaled_width, scaled_height)))
-                    x += scaled_width
+            if x + width <= max_width:
+                return x
 
-                y += row_height
-                candidate_layout.append(final_row)
-
-            if max_width == 0:
-                max_width = x
-
-            return max_width, y, candidate_layout
-
-        canvas_aspect_x, canvas_aspect_y = self.canvas.get_aspect(coefficient)
-        camera_layout: list[list[Any]] = []
-        camera_layout.append([])
-        starting_x = 0
-        x = starting_x
-        y = 0
-        y_i = 0
-        max_y = 0
-        for camera in cameras_to_add:
-            camera_dims = self.cameras[camera]["dimensions"].copy()
-            camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
-                camera, camera_dims[0], camera_dims[1]
-            )
-
-            if camera_dims[1] > camera_dims[0]:
-                portrait = True
-            else:
-                portrait = False
-
-            if (x + camera_aspect_x) <= canvas_aspect_x:
-                # insert if camera can fit on current row
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-
-                if portrait:
-                    starting_x = camera_aspect_x
-                else:
-                    max_y = max(
-                        max_y,
-                        camera_aspect_y,
-                    )
-
-                x += camera_aspect_x
-            else:
-                # move on to the next row and insert
-                y += max_y
-                y_i += 1
-                camera_layout.append([])
-                x = starting_x
-
-                if x + camera_aspect_x > canvas_aspect_x:
-                    return None
-
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-                x += camera_aspect_x
-
-        if y + max_y > canvas_aspect_y:
             return None
 
-        row_height = int(self.canvas.height / coefficient)
-        total_width, total_height, standard_candidate_layout = map_layout(
-            camera_layout, row_height
-        )
+        def map_layout(row_height: int) -> tuple[int, int, list[list[Any]] | None]:
+            """Lay out cameras row by row while reserving portrait spans for the next row."""
+            candidate_layout: list[list[Any]] = []
+            reserved_ranges: dict[int, list[tuple[int, int]]] = {}
+            current_row: list[Any] = []
+            row_index = 0
+            row_y = 0
+            row_x = 0
+            max_width = 0
+            max_height = 0
+
+            for camera in cameras_to_add:
+                camera_dims = self.cameras[camera]["dimensions"].copy()
+                camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
+                    camera, camera_dims[0], camera_dims[1]
+                )
+                portrait = camera_dims[1] > camera_dims[0]
+                scaled_height = row_height * 2 if portrait else row_height
+                scaled_width = int(scaled_height * (camera_aspect_x / camera_aspect_y))
+
+                while True:
+                    x = find_available_x(
+                        row_x,
+                        scaled_width,
+                        reserved_ranges.get(row_index, []),
+                        self.canvas.width,
+                    )
+
+                    if x is not None and row_y + scaled_height <= self.canvas.height:
+                        current_row.append(
+                            (camera, (x, row_y, scaled_width, scaled_height))
+                        )
+                        row_x = x + scaled_width
+                        max_width = max(max_width, row_x)
+                        max_height = max(max_height, row_y + scaled_height)
+
+                        if portrait:
+                            reserved_ranges.setdefault(row_index + 1, []).append(
+                                (x, row_x)
+                            )
+
+                        break
+
+                    if current_row:
+                        candidate_layout.append(current_row)
+                        current_row = []
+
+                    row_index += 1
+                    row_y = row_index * row_height
+                    row_x = 0
+
+                    if row_y + scaled_height > self.canvas.height:
+                        overflow_width = max(max_width, scaled_width)
+                        overflow_height = row_y + scaled_height
+                        return overflow_width, overflow_height, None
+
+            if current_row:
+                candidate_layout.append(current_row)
+
+            return max_width, max_height, candidate_layout
+
+        row_height = max(1, int(self.canvas.height / coefficient))
+        total_width, total_height, standard_candidate_layout = map_layout(row_height)
 
         if not standard_candidate_layout:
             # if standard layout didn't work
@@ -718,9 +706,9 @@ class BirdsEyeFrameManager:
                 total_width / self.canvas.width,
                 total_height / self.canvas.height,
             )
-            row_height = int(row_height / scale_down_percent)
+            row_height = max(1, int(row_height / scale_down_percent))
             total_width, total_height, standard_candidate_layout = map_layout(
-                camera_layout, row_height
+                row_height
             )
 
             if not standard_candidate_layout:
@@ -734,8 +722,8 @@ class BirdsEyeFrameManager:
             1 / (total_width / self.canvas.width),
             1 / (total_height / self.canvas.height),
         )
-        row_height = int(row_height * scale_up_percent)
-        _, _, scaled_layout = map_layout(camera_layout, row_height)
+        row_height = max(1, int(row_height * scale_up_percent))
+        _, _, scaled_layout = map_layout(row_height)
 
         if scaled_layout:
             return scaled_layout
@@ -745,8 +733,7 @@ class BirdsEyeFrameManager:
     def update(
         self,
         camera: str,
-        object_count: int,
-        motion_count: int,
+        activity: BirdseyeActivity,
         frame_time: float,
         frame: np.ndarray,
     ) -> tuple[bool, bool]:
@@ -774,7 +761,10 @@ class BirdsEyeFrameManager:
         # update the last active frame for the camera
         self.cameras[camera]["current_frame"] = frame.copy()
         self.cameras[camera]["current_frame_time"] = frame_time
-        if self.camera_active(camera_config.birdseye.mode, object_count, motion_count):
+        if self.camera_active(
+            camera_config.birdseye.mode,
+            activity,
+        ):
             self.cameras[camera]["last_active_frame"] = frame_time
 
         now = datetime.datetime.now().timestamp()
@@ -882,10 +872,29 @@ class Birdseye:
         frame_time: float,
         frame: np.ndarray,
     ) -> None:
+        has_active_object = False
+        has_stationary_object = False
+        for tracked_object in current_tracked_objects:
+            if tracked_object["stationary"]:
+                if not tracked_object["false_positive"]:
+                    has_stationary_object = True
+            else:
+                # Preserve the existing objects activity behavior, which includes
+                # non-stationary trackers before they are confirmed.
+                has_active_object = True
+
+            if has_active_object and has_stationary_object:
+                break
+
+        activity = BirdseyeActivity(
+            has_active_object=has_active_object,
+            has_stationary_object=has_stationary_object,
+            has_motion=bool(motion_boxes),
+        )
+
         frame_changed, frame_layout_changed = self.birdseye_manager.update(
             camera,
-            len([o for o in current_tracked_objects if not o["stationary"]]),
-            len(motion_boxes),
+            activity,
             frame_time,
             frame,
         )

frigate/output/output.py

@@ -178,13 +178,10 @@ class OutputProcess(FrigateProcess):
             )
 
             if update_topic is not None and birdseye_config is not None:
-                previous_global_mode = self.config.birdseye.mode
+                # only the global-only fields are applied here; the per-camera
+                # enabled and mode arrive on config/cameras/<name>/birdseye,
+                # already resolved against yaml by the config parse
                 self.config.birdseye = birdseye_config
-
-                for camera_config in self.config.cameras.values():
-                    if camera_config.birdseye.mode == previous_global_mode:
-                        camera_config.birdseye.mode = birdseye_config.mode
-
                 logger.debug("Applied dynamic birdseye config update")
 
             # check if there is an updated config

frigate/ptz/onvif.py

@@ -625,14 +625,18 @@ class OnvifController:
             return
 
         self.cams[camera_name]["active"] = True
-        self.ptz_metrics[camera_name].motor_stopped.clear()
-        logger.debug(
-            f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
-        )
-        self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
-            camera_name
-        ].frame_time.value
-        self.ptz_metrics[camera_name].stop_time.value = 0
+
+        # only track start_time for autotracking
+        if self.ptz_metrics[camera_name].autotracker_enabled.value:
+            self.ptz_metrics[camera_name].motor_stopped.clear()
+            logger.debug(
+                f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
+            )
+            self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
+                camera_name
+            ].frame_time.value
+            self.ptz_metrics[camera_name].stop_time.value = 0
+
         move_request = self.cams[camera_name]["relative_move_request"]
 
         # function takes in -1 to 1 for pan and tilt, interpolate to the values of the camera.

frigate/review/maintainer.py

@@ -392,6 +392,32 @@ class ReviewSegmentMaintainer(threading.Thread):
             return self._publish_segment_end(segment, prev_data)
         return None
 
+    def get_manual_event_severity(self, camera: str, label: str) -> SeverityEnum | None:
+        """Determine the review severity for a manual event label.
+
+        Alert labels take precedence over detection labels, matching how
+        tracked objects are categorized. Labels in neither list default to
+        alerts so manual events keep their historical severity.
+        """
+        review_config = self.config.cameras[camera].review
+        # label contains 'label: sub_label', only the label is categorized
+        label = label.split(": ")[0]
+
+        if review_config.alerts.enabled and label in review_config.alerts.labels:
+            return SeverityEnum.alert
+
+        if (
+            review_config.detections.enabled
+            and review_config.detections.labels is not None
+            and label in review_config.detections.labels
+        ):
+            return SeverityEnum.detection
+
+        if review_config.alerts.enabled:
+            return SeverityEnum.alert
+
+        return None
+
     def update_existing_segment(
         self,
         segment: PendingReviewSegment,
@@ -450,7 +476,7 @@ class ReviewSegmentMaintainer(threading.Thread):
 
                 if not object["sub_label"]:
                     segment.detections[object["id"]] = object["label"]
-                elif object["sub_label"][0] in self.config.model.all_attributes:
+                elif object["sub_label"][0] in self.config.all_attributes:
                     segment.detections[object["id"]] = object["sub_label"][0]
                 else:
                     segment.detections[object["id"]] = f"{object['label']}-verified"
@@ -588,7 +614,7 @@ class ReviewSegmentMaintainer(threading.Thread):
             for object in activity.get_all_objects():
                 if not object["sub_label"]:
                     detections[object["id"]] = object["label"]
-                elif object["sub_label"][0] in self.config.model.all_attributes:
+                elif object["sub_label"][0] in self.config.all_attributes:
                     detections[object["id"]] = object["sub_label"][0]
                 else:
                     detections[object["id"]] = f"{object['label']}-verified"
@@ -734,24 +760,19 @@ class ReviewSegmentMaintainer(threading.Thread):
                             manual_info["label"]
                         )
                         if topic == DetectionTypeEnum.api:
-                            # manual_info["label"] contains 'label: sub_label'
-                            # so split out the label without modifying manual_info
-                            det_labels = self.config.cameras[
-                                camera
-                            ].review.detections.labels
-                            if (
-                                self.config.cameras[camera].review.detections.enabled
-                                and det_labels is not None
-                                and manual_info["label"].split(": ")[0] in det_labels
-                            ):
-                                current_segment.last_detection_time = manual_info[
-                                    "end_time"
-                                ]
-                            elif self.config.cameras[camera].review.alerts.enabled:
+                            severity = self.get_manual_event_severity(
+                                camera, manual_info["label"]
+                            )
+
+                            if severity == SeverityEnum.alert:
                                 current_segment.severity = SeverityEnum.alert
                                 current_segment.last_alert_time = manual_info[
                                     "end_time"
                                 ]
+                            elif severity == SeverityEnum.detection:
+                                current_segment.last_detection_time = manual_info[
+                                    "end_time"
+                                ]
                         elif (
                             topic == DetectionTypeEnum.lpr
                             and self.config.cameras[camera].review.detections.enabled
@@ -765,21 +786,12 @@ class ReviewSegmentMaintainer(threading.Thread):
                         current_segment.detections[manual_info["event_id"]] = (
                             manual_info["label"]
                         )
-                        if (
-                            topic == DetectionTypeEnum.api
-                            and self.config.cameras[camera].review.alerts.enabled
-                        ):
-                            # manual_info["label"] contains 'label: sub_label'
-                            # so split out the label without modifying manual_info
-                            det_labels = self.config.cameras[
-                                camera
-                            ].review.detections.labels
+                        if topic == DetectionTypeEnum.api:
                             if (
-                                not self.config.cameras[
-                                    camera
-                                ].review.detections.enabled
-                                or det_labels is None
-                                or manual_info["label"].split(": ")[0] not in det_labels
+                                self.get_manual_event_severity(
+                                    camera, manual_info["label"]
+                                )
+                                == SeverityEnum.alert
                             ):
                                 current_segment.severity = SeverityEnum.alert
                         elif (
@@ -853,18 +865,9 @@ class ReviewSegmentMaintainer(threading.Thread):
                             detections,
                         )
                 elif topic == DetectionTypeEnum.api:
-                    severity = None
-                    # manual_info["label"] contains 'label: sub_label'
-                    # so split out the label without modifying manual_info
-                    det_labels = self.config.cameras[camera].review.detections.labels
-                    if (
-                        self.config.cameras[camera].review.detections.enabled
-                        and det_labels is not None
-                        and manual_info["label"].split(": ")[0] in det_labels
-                    ):
-                        severity = SeverityEnum.detection
-                    elif self.config.cameras[camera].review.alerts.enabled:
-                        severity = SeverityEnum.alert
+                    severity = self.get_manual_event_severity(
+                        camera, manual_info["label"]
+                    )
 
                     if severity:
                         api_segment = PendingReviewSegment(

frigate/stats/util.py

@@ -62,7 +62,7 @@ def get_latest_version(config: FrigateConfig) -> str:
 def stats_init(
     config: FrigateConfig,
     camera_metrics: DictProxy,
-    embeddings_metrics: DataProcessorMetrics | None,
+    embeddings_metrics: DataProcessorMetrics,
     detectors: dict[str, ObjectDetectProcess],
     processes: dict[str, int],
 ) -> StatsTrackingTypes:
@@ -322,19 +322,20 @@ async def set_gpu_stats(
 async def set_npu_usages(config: FrigateConfig, all_stats: dict[str, Any]) -> None:
     stats: dict[str, dict] = {}
 
-    for detector in config.detectors.values():
-        if detector.type == "rknn":
-            # Rockchip NPU usage
-            rk_usage = get_rockchip_npu_stats()
-            stats["rockchip"] = rk_usage
-        elif detector.type == "openvino" and detector.device == "NPU":
-            # OpenVINO NPU usage
-            ov_usage = get_openvino_npu_stats()
-            stats["openvino"] = ov_usage
-        elif detector.type == "axengine":
-            # AXERA NPU usage
-            axcl_usage = get_axcl_npu_stats()
-            stats["axengine"] = axcl_usage
+    for model in config.models:
+        for device in config.devices_for_model(model):
+            if device.detector == "rknn":
+                # Rockchip NPU usage
+                rk_usage = get_rockchip_npu_stats()
+                stats["rockchip"] = rk_usage
+            elif device.detector == "openvino" and device.device == "NPU":
+                # OpenVINO NPU usage
+                ov_usage = get_openvino_npu_stats()
+                stats["openvino"] = ov_usage
+            elif device.detector == "axengine":
+                # AXERA NPU usage
+                axcl_usage = get_axcl_npu_stats()
+                stats["axengine"] = axcl_usage
 
     if stats:
         all_stats["npu_usages"] = stats

frigate/test/http_api/test_http_auth_internal_port.py

@@ -0,0 +1,174 @@
+"""Tests that the internal port trusted by /auth cannot be moved at runtime."""
+
+import os
+import tempfile
+import unittest
+from unittest.mock import MagicMock, Mock, patch
+
+import ruamel.yaml
+from fastapi import Request
+
+from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
+from frigate.api.fastapi_app import create_fastapi_app
+from frigate.config import FrigateConfig
+from frigate.config.camera.updater import CameraConfigUpdatePublisher
+from frigate.const import JWT_SECRET_ENV_VAR
+from frigate.models import Event, Recordings, ReviewSegment
+from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
+
+
+@patch.dict(os.environ, {JWT_SECRET_ENV_VAR: "test-secret"})
+class TestAuthInternalPort(BaseTestHttp):
+    """/auth grants anonymous admin by port, so that port must stay put.
+
+    nginx binds its listeners once at container start and never reloads them,
+    but /api/config/set can swap the live config object mid-process. If /auth
+    read the port off the live config, saving networking.listen.internal would
+    hand unauthenticated admin to whoever can reach the external port.
+    """
+
+    def setUp(self):
+        super().setUp(models=[Event, Recordings, ReviewSegment])
+        self.minimal_config = {
+            "mqtt": {"host": "mqtt"},
+            "auth": {"enabled": True},
+            "networking": {"listen": {"internal": 5000, "external": 8971}},
+            "cameras": {
+                "front_door": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
+                        ]
+                    },
+                    "detect": {
+                        "height": 1080,
+                        "width": 1920,
+                        "fps": 5,
+                    },
+                }
+            },
+        }
+
+    def _create_app(self):
+        mock_publisher = Mock(spec=CameraConfigUpdatePublisher)
+        mock_publisher.publisher = MagicMock()
+
+        app = create_fastapi_app(
+            FrigateConfig(**self.minimal_config),
+            self.db,
+            None,
+            None,
+            None,
+            None,
+            None,
+            None,
+            mock_publisher,
+            None,
+            enforce_default_admin=False,
+        )
+
+        async def mock_get_current_user(request: Request):
+            return {
+                "username": request.headers.get("remote-user"),
+                "role": request.headers.get("remote-role"),
+            }
+
+        async def mock_get_allowed_cameras_for_filter(request: Request):
+            return list(self.minimal_config.get("cameras", {}).keys())
+
+        app.dependency_overrides[get_current_user] = mock_get_current_user
+        app.dependency_overrides[get_allowed_cameras_for_filter] = (
+            mock_get_allowed_cameras_for_filter
+        )
+
+        return app
+
+    def _write_config_file(self):
+        """Write the minimal config to a temp YAML file and return the path."""
+        yaml = ruamel.yaml.YAML()
+        f = tempfile.NamedTemporaryFile(mode="w", suffix=".yml", delete=False)
+        yaml.dump(self.minimal_config, f)
+        f.close()
+        return f.name
+
+    def test_internal_port_is_anonymous_admin(self):
+        app = self._create_app()
+
+        with AuthTestClient(app) as client:
+            resp = client.get("/auth", headers={"x-server-port": "5000"})
+
+            self.assertEqual(resp.status_code, 202)
+            self.assertEqual(resp.headers["remote-user"], "anonymous")
+            self.assertEqual(resp.headers["remote-role"], "admin")
+
+    def test_external_port_requires_auth(self):
+        app = self._create_app()
+
+        with AuthTestClient(app) as client:
+            resp = client.get("/auth", headers={"x-server-port": "8971"})
+
+            self.assertEqual(resp.status_code, 401)
+
+    def test_swapped_config_does_not_move_the_trusted_port(self):
+        """The live config is not what /auth trusts.
+
+        Stands in for every path that can rebind app.frigate_config while the
+        process runs, whatever restart flag the caller claimed.
+        """
+        app = self._create_app()
+
+        swapped = FrigateConfig(
+            **{
+                **self.minimal_config,
+                "networking": {"listen": {"internal": 8971, "external": 5000}},
+            }
+        )
+        app.frigate_config = swapped
+
+        with AuthTestClient(app) as client:
+            resp = client.get("/auth", headers={"x-server-port": "8971"})
+            self.assertEqual(resp.status_code, 401)
+
+            # nginx is still listening where it was told to at boot
+            resp = client.get("/auth", headers={"x-server-port": "5000"})
+            self.assertEqual(resp.status_code, 202)
+            self.assertEqual(resp.headers["remote-role"], "admin")
+
+    @patch("frigate.api.app.find_config_file")
+    def test_config_set_rejects_internal_matching_external(self, mock_find_config):
+        """Saving the internal port onto the external one is refused outright."""
+        config_path = self._write_config_file()
+        mock_find_config.return_value = config_path
+
+        try:
+            app = self._create_app()
+
+            with AuthTestClient(app) as client:
+                resp = client.put(
+                    "/config/set",
+                    json={
+                        "config_data": {"networking": {"listen": {"internal": 8971}}},
+                        "update_topic": "config/networking",
+                        "requires_restart": 1,
+                    },
+                )
+
+                self.assertEqual(resp.status_code, 400)
+                self.assertFalse(resp.json()["success"])
+
+                # the rejected save must not have reached the live config
+                self.assertEqual(
+                    app.frigate_config.networking.listen.internal_port, 5000
+                )
+
+                resp = client.get("/auth", headers={"x-server-port": "8971"})
+                self.assertEqual(resp.status_code, 401)
+
+            with open(config_path) as f:
+                self.assertNotIn("8971", f.read().split("external")[0])
+        finally:
+            os.unlink(config_path)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

frigate/test/http_api/test_http_classification_traversal.py

@@ -0,0 +1,73 @@
+"""End to end checks that classification endpoints cannot escape their base dir."""
+
+import os
+import shutil
+import tempfile
+from unittest.mock import patch
+
+from frigate.models import Event
+from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
+
+# Percent encodings that survive nginx normalization. nginx collapses a bare
+# ".." segment, but "..:" and friends are not relative segments to nginx while
+# pathvalidate still reduces them to exactly "..".
+TRAVERSAL_NAMES = ["..%3A", "..%2A", "..%3C", "..%7C", "..%20", ".."]
+
+
+class TestHttpClassificationTraversal(BaseTestHttp):
+    def setUp(self):
+        super().setUp([Event])
+        self.app = super().create_app()
+
+        self.root = tempfile.mkdtemp()
+        self.clips = os.path.join(self.root, "clips")
+        self.model_cache = os.path.join(self.root, "model_cache")
+        os.makedirs(os.path.join(self.clips, "model1"))
+        os.makedirs(os.path.join(self.model_cache, "model1"))
+        os.makedirs(os.path.join(self.root, "recordings"))
+
+        # Sibling data that a "/.." escape from clips would reach.
+        self.canary = os.path.join(self.root, "recordings", "seg.mp4")
+
+        with open(self.canary, "w") as f:
+            f.write("recording")
+
+        clips_patch = patch("frigate.api.classification.CLIPS_DIR", self.clips)
+        cache_patch = patch(
+            "frigate.api.classification.MODEL_CACHE_DIR", self.model_cache
+        )
+        clips_patch.start()
+        cache_patch.start()
+        self.addCleanup(clips_patch.stop)
+        self.addCleanup(cache_patch.stop)
+
+    def tearDown(self):
+        shutil.rmtree(self.root, ignore_errors=True)
+        self.app.dependency_overrides.clear()
+        super().tearDown()
+
+    def test_delete_model_rejects_traversal_names(self):
+        client = AuthTestClient(self.app)
+
+        for name in TRAVERSAL_NAMES:
+            with self.subTest(name=name):
+                response = client.delete(f"/classification/{name}")
+
+                # Either the router never matches it or the handler rejects it,
+                # but the sibling directory must survive either way.
+                self.assertNotEqual(response.status_code, 200)
+                self.assertTrue(
+                    os.path.exists(self.canary),
+                    f"{name} deleted data outside the clips directory",
+                )
+                self.assertTrue(os.path.exists(os.path.join(self.root, "recordings")))
+
+    def test_delete_model_still_removes_its_own_directories(self):
+        client = AuthTestClient(self.app)
+
+        response = client.delete("/classification/model1")
+
+        self.assertEqual(response.status_code, 200)
+        self.assertFalse(os.path.exists(os.path.join(self.clips, "model1")))
+        self.assertFalse(os.path.exists(os.path.join(self.model_cache, "model1")))
+        self.assertTrue(os.path.exists(self.canary))

frigate/test/http_api/test_http_config_set.py

@@ -13,6 +13,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdatePublisher,
     CameraConfigUpdateTopic,
 )
+from frigate.config.holder import ConfigHolder
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
 
@@ -373,6 +374,151 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
         finally:
             os.unlink(config_path)
 
+    @patch("frigate.api.app.find_config_file")
+    def test_global_birdseye_save_fans_out_resolved_camera_configs(
+        self, mock_find_config
+    ):
+        """A global birdseye save must also publish the per-camera values.
+
+        Global birdseye only seeds enabled and mode; the camera copies are what
+        the output process actually reads. Sending just the global object makes
+        a worker guess which cameras were inheriting, and the only available
+        guess (mode still equals the previous global) wrongly claims a camera
+        whose explicit yaml mode happens to match.
+        """
+        self.minimal_config["birdseye"] = {
+            "enabled": True,
+            "mode": {"motion": True},
+        }
+        # explicit override that matches the global value being replaced
+        self.minimal_config["cameras"]["front_door"]["birdseye"] = {
+            "mode": {
+                "continuous": False,
+                "motion": True,
+                "objects": False,
+                "stationary_objects": False,
+            }
+        }
+
+        config_path = self._write_config_file()
+        mock_find_config.return_value = config_path
+
+        try:
+            app, mock_publisher = self._create_app_with_publisher()
+            with AuthTestClient(app) as client:
+                resp = client.put(
+                    "/config/set",
+                    json={
+                        "config_data": {
+                            "birdseye": {
+                                "mode": {
+                                    "continuous": True,
+                                    "motion": False,
+                                    "objects": False,
+                                    "stationary_objects": False,
+                                }
+                            }
+                        },
+                        "update_topic": "config/birdseye",
+                        "requires_restart": 0,
+                    },
+                )
+
+                self.assertEqual(resp.status_code, 200)
+
+                # the global object still goes out on its own topic
+                mock_publisher.publisher.publish.assert_called_once()
+                topic, settings = mock_publisher.publisher.publish.call_args[0]
+                self.assertEqual(topic, "config/birdseye")
+                self.assertEqual(settings.mode.to_mqtt_payload(), "CONTINUOUS")
+
+                published = {
+                    call[0][0].camera: call[0][1]
+                    for call in mock_publisher.publish_update.call_args_list
+                }
+                self.assertEqual(set(published), {"front_door", "back_yard"})
+
+                for call in mock_publisher.publish_update.call_args_list:
+                    self.assertEqual(
+                        call[0][0].update_type, CameraConfigUpdateEnum.birdseye
+                    )
+
+                # the override survives, the inheriting camera follows global
+                self.assertEqual(
+                    published["front_door"].mode.to_mqtt_payload(), "MOTION"
+                )
+                self.assertEqual(
+                    published["back_yard"].mode.to_mqtt_payload(), "CONTINUOUS"
+                )
+        finally:
+            os.unlink(config_path)
+
+    @patch("frigate.api.app.find_config_file")
+    def test_save_updates_the_config_holder(self, mock_find_config):
+        """A save must move the holder onto the freshly parsed config.
+
+        FrigateApp reads the holder when the watchdog rebuilds a crashed
+        process; if the save leaves it on the boot config, that process comes
+        back having lost every change made since Frigate started.
+        """
+        from fastapi import Request
+
+        from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
+        from frigate.api.fastapi_app import create_fastapi_app
+
+        config_path = self._write_config_file()
+        mock_find_config.return_value = config_path
+
+        mock_publisher = Mock(spec=CameraConfigUpdatePublisher)
+        mock_publisher.publisher = MagicMock()
+        boot_config = FrigateConfig(**self.minimal_config)
+        holder = ConfigHolder(boot_config)
+
+        try:
+            app = create_fastapi_app(
+                boot_config,
+                self.db,
+                None,
+                None,
+                None,
+                None,
+                None,
+                None,
+                mock_publisher,
+                None,
+                enforce_default_admin=False,
+                config_holder=holder,
+            )
+
+            async def mock_get_current_user(request: Request):
+                return {"username": "admin", "role": "admin"}
+
+            async def mock_get_allowed_cameras_for_filter(request: Request):
+                return list(self.minimal_config.get("cameras", {}).keys())
+
+            app.dependency_overrides[get_current_user] = mock_get_current_user
+            app.dependency_overrides[get_allowed_cameras_for_filter] = (
+                mock_get_allowed_cameras_for_filter
+            )
+
+            with AuthTestClient(app) as client:
+                resp = client.put(
+                    "/config/set",
+                    json={
+                        "config_data": {"birdseye": {"inactivity_threshold": 5}},
+                        "update_topic": "config/birdseye",
+                        "requires_restart": 0,
+                    },
+                )
+
+                self.assertEqual(resp.status_code, 200)
+
+            self.assertIsNot(holder.config, boot_config)
+            self.assertIs(holder.config, app.frigate_config)
+            self.assertEqual(holder.config.birdseye.inactivity_threshold, 5)
+        finally:
+            os.unlink(config_path)
+
 
 if __name__ == "__main__":
     unittest.main()

frigate/test/http_api/test_http_media.py

@@ -1,15 +1,73 @@
 """Unit tests for recordings/media API endpoints."""
 
+from dataclasses import dataclass
 from datetime import UTC, datetime
+from unittest.mock import patch
 
 import pytz
 from fastapi import Request
 
 from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
+from frigate.const import MAX_SEGMENT_DURATION
 from frigate.models import Recordings
 from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
 
 
+@dataclass(frozen=True)
+class RangeCase:
+    """Expected behavior for one segment relative to the requested range.
+
+    Offsets are seconds from REQUEST_START; the request ends at +100 seconds.
+    """
+
+    name: str
+    start_offset: float
+    end_offset: float
+    included_in_recordings: bool
+    vod_clip_from_ms: int | None = None
+    vod_duration_ms: int | None = None
+
+
+REQUEST_START = 1000
+REQUEST_END = 1100
+RANGE_CASES = (
+    RangeCase("before", -MAX_SEGMENT_DURATION + 1, -1, False),
+    RangeCase("meets_start", -10, 0, True),
+    RangeCase(
+        "overlaps_start",
+        -MAX_SEGMENT_DURATION + 0.5,
+        0.25,
+        True,
+        vod_clip_from_ms=599500,
+        vod_duration_ms=250,
+    ),
+    RangeCase("starts_at_start", 0, 10, True, vod_duration_ms=10000),
+    RangeCase("inside", 20, 80, True, vod_duration_ms=60000),
+    RangeCase("ends_at_end", 90, 100, True, vod_duration_ms=10000),
+    RangeCase("matches_range", 0, 100, True, vod_duration_ms=100000),
+    RangeCase("starts_with_range", 0, 110, True, vod_duration_ms=100000),
+    RangeCase(
+        "covers_range",
+        -20,
+        120,
+        True,
+        vod_clip_from_ms=20000,
+        vod_duration_ms=100000,
+    ),
+    RangeCase(
+        "ends_with_range",
+        -10,
+        100,
+        True,
+        vod_clip_from_ms=10000,
+        vod_duration_ms=100000,
+    ),
+    RangeCase("overlaps_end", 95, 105, True, vod_duration_ms=5000),
+    RangeCase("starts_at_end", 100, 110, True),
+    RangeCase("after", 101, 110, False),
+)
+
+
 class TestHttpMedia(BaseTestHttp):
     """Test media API endpoints, particularly recordings with DST handling."""
 
@@ -44,6 +102,26 @@ class TestHttpMedia(BaseTestHttp):
         self.app.dependency_overrides.clear()
         super().tearDown()
 
+    def _assert_vod_response(
+        self,
+        response,
+        expected_clips: list[tuple[str, int | None, int]],
+    ) -> None:
+        """Assert VOD clip metadata and its derived duration fields."""
+        assert response.status_code == 200
+        vod = response.json()
+        assert [
+            (
+                clip["path"],
+                clip.get("clipFrom"),
+                clip["keyFrameDurations"][0],
+            )
+            for clip in vod["sequences"][0]["clips"]
+        ] == expected_clips
+        expected_durations = [clip[2] for clip in expected_clips]
+        assert vod["durations"] == expected_durations
+        assert vod["segment_duration"] == max(expected_durations)
+
     def test_recordings_summary_across_dst_spring_forward(self):
         """
         Test recordings summary across spring DST transition (spring forward).
@@ -404,6 +482,102 @@ class TestHttpMedia(BaseTestHttp):
             assert "2024-03-10" in summary
             assert summary["2024-03-10"] is True
 
+    def test_recordings_handles_all_range_relations(self):
+        """Recordings return every interval relation that touches the range."""
+        with AuthTestClient(self.app) as client:
+            for case in RANGE_CASES:
+                with self.subTest(case=case.name):
+                    Recordings.delete().execute()
+                    super().insert_mock_recording(
+                        case.name,
+                        REQUEST_START + case.start_offset,
+                        REQUEST_START + case.end_offset,
+                    )
+
+                    response = client.get(
+                        "/front_door/recordings",
+                        params={"after": REQUEST_START, "before": REQUEST_END},
+                    )
+
+                    assert response.status_code == 200
+                    expected_ids = [case.name] if case.included_in_recordings else []
+                    assert [
+                        recording["id"] for recording in response.json()
+                    ] == expected_ids
+
+    def test_vod_handles_all_range_relations(self):
+        """VOD clips every interval relation with positive playback duration."""
+        with (
+            AuthTestClient(self.app) as client,
+            patch(
+                "frigate.api.media.get_keyframe_before",
+                side_effect=lambda _path, offset: offset,
+            ),
+        ):
+            for case in RANGE_CASES:
+                with self.subTest(case=case.name):
+                    Recordings.delete().execute()
+                    super().insert_mock_recording(
+                        case.name,
+                        REQUEST_START + case.start_offset,
+                        REQUEST_START + case.end_offset,
+                    )
+
+                    response = client.get(
+                        f"/vod/front_door/start/{REQUEST_START}/end/{REQUEST_END}"
+                    )
+
+                    if case.vod_duration_ms is None:
+                        assert response.status_code == 404
+                        continue
+
+                    self._assert_vod_response(
+                        response,
+                        [
+                            (
+                                case.name,
+                                case.vod_clip_from_ms,
+                                case.vod_duration_ms,
+                            )
+                        ],
+                    )
+
+    def test_vod_handles_segment_ending_at_start_with_keyframe_fallbacks(self):
+        """VOD keeps a boundary segment when keyframe lookup extends it."""
+
+        def keyframe_before(path: str, offset: int) -> int | None:
+            return offset - 1000 if path == "previous_keyframe" else None
+
+        with (
+            AuthTestClient(self.app) as client,
+            patch(
+                "frigate.api.media.get_keyframe_before",
+                side_effect=keyframe_before,
+            ),
+        ):
+            super().insert_mock_recording(
+                "previous_keyframe",
+                REQUEST_START - 10,
+                REQUEST_START,
+            )
+            super().insert_mock_recording(
+                "missing_keyframe",
+                REQUEST_START - 5,
+                REQUEST_START,
+            )
+
+            response = client.get(
+                f"/vod/front_door/start/{REQUEST_START}/end/{REQUEST_END}"
+            )
+
+            self._assert_vod_response(
+                response,
+                [
+                    ("previous_keyframe", 9000, 1000),
+                    ("missing_keyframe", None, 5000),
+                ],
+            )
+
     def test_recordings_unavailable_reports_gap_between_recordings(self):
         """A gap between two recordings is reported as an unavailable segment."""
         with AuthTestClient(self.app) as client:

frigate/test/test_birdseye.py

@@ -1,13 +1,70 @@
-"""Test camera user and password cleanup."""
+"""Tests for Birdseye canvas sizing and layout behavior."""
 
 import multiprocessing as mp
 import unittest
+from unittest.mock import Mock
 
-from frigate.config import FrigateConfig
-from frigate.output.birdseye import BirdsEyeFrameManager, get_canvas_shape
+from frigate.config import BirdseyeModeConfig, FrigateConfig
+from frigate.output.birdseye import (
+    Birdseye,
+    BirdseyeActivity,
+    BirdsEyeFrameManager,
+    get_canvas_shape,
+)
 
 
 class TestBirdseye(unittest.TestCase):
+    def _build_manager(
+        self, camera_dimensions: dict[str, tuple[int, int]]
+    ) -> BirdsEyeFrameManager:
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "birdseye": {"width": 1280, "height": 720},
+            "cameras": {},
+        }
+
+        for order, (camera, dimensions) in enumerate(
+            camera_dimensions.items(), start=1
+        ):
+            config["cameras"][camera] = {
+                "ffmpeg": {
+                    "inputs": [
+                        {
+                            "path": f"rtsp://10.0.0.1:554/{camera}",
+                            "roles": ["detect"],
+                        }
+                    ]
+                },
+                "detect": {
+                    "width": dimensions[0],
+                    "height": dimensions[1],
+                    "fps": 5,
+                },
+                "birdseye": {"order": order},
+            }
+
+        return BirdsEyeFrameManager(FrigateConfig(**config), mp.Event())
+
+    def _assert_no_overlaps(
+        self, layout: list[list[tuple[str, tuple[int, int, int, int]]]]
+    ):
+        rectangles = [position for row in layout for _, position in row]
+
+        for index, rect in enumerate(rectangles):
+            x1, y1, width1, height1 = rect
+            for other in rectangles[index + 1 :]:
+                x2, y2, width2, height2 = other
+                overlap = (
+                    x1 < x2 + width2
+                    and x2 < x1 + width1
+                    and y1 < y2 + height2
+                    and y2 < y1 + height1
+                )
+                self.assertFalse(
+                    overlap,
+                    msg=f"Overlapping rectangles found: {rect} and {other}",
+                )
+
     def test_16x9(self):
         """Test 16x9 aspect ratio works as expected for birdseye."""
         width = 1280
@@ -48,6 +105,212 @@ class TestBirdseye(unittest.TestCase):
         assert canvas_width == width  # width will be the same
         assert canvas_height != height
 
+    def test_portrait_camera_does_not_overlap_next_row(self):
+        """Portrait cameras should reserve their real horizontal position on the next row."""
+        manager = self._build_manager(
+            {
+                "cam_a": (1280, 720),
+                "cam_p": (360, 640),
+                "cam_b": (1280, 720),
+                "cam_c": (640, 480),
+            }
+        )
+
+        layout = manager.calculate_layout(["cam_a", "cam_p", "cam_b", "cam_c"], 3)
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+        cam_c = [
+            position for row in layout for camera, position in row if camera == "cam_c"
+        ][0]
+        self.assertEqual(cam_c[0], 0)
+
+    def test_portrait_reservation_only_applies_to_next_row(self):
+        """Portrait reservations should not push later rows after the span ends."""
+        manager = self._build_manager(
+            {
+                "cam_a": (1280, 720),
+                "cam_p": (360, 640),
+                "cam_b": (1280, 720),
+                "cam_c": (1280, 720),
+                "cam_d": (1280, 720),
+                "cam_e": (1280, 720),
+            }
+        )
+
+        layout = manager.calculate_layout(
+            ["cam_a", "cam_p", "cam_b", "cam_c", "cam_d", "cam_e"],
+            3,
+        )
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+        cam_e = [
+            position for row in layout for camera, position in row if camera == "cam_e"
+        ][0]
+        self.assertEqual(cam_e[0], 0)
+
+    def test_multiple_portraits_reserve_distinct_ranges(self):
+        """Multiple portrait cameras in one row should reserve separate spans below them."""
+        manager = self._build_manager(
+            {
+                "cam_a": (640, 480),
+                "cam_p1": (360, 640),
+                "cam_p2": (360, 640),
+                "cam_b": (640, 480),
+                "cam_c": (1280, 720),
+                "cam_d": (640, 480),
+            }
+        )
+
+        layout = manager.calculate_layout(
+            ["cam_a", "cam_p1", "cam_p2", "cam_b", "cam_c", "cam_d"],
+            4,
+        )
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+    def test_two_landscapes_then_portrait_then_two_landscapes(self):
+        """A portrait after two landscapes should reserve only its own tail span."""
+        manager = self._build_manager(
+            {
+                "cam_a": (1280, 720),
+                "cam_b": (1280, 720),
+                "cam_p": (360, 640),
+                "cam_c": (1280, 720),
+                "cam_d": (1280, 720),
+            }
+        )
+
+        layout = manager.calculate_layout(
+            ["cam_a", "cam_b", "cam_p", "cam_c", "cam_d"],
+            3,
+        )
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+        cam_c = [
+            position for row in layout for camera, position in row if camera == "cam_c"
+        ][0]
+        cam_d = [
+            position for row in layout for camera, position in row if camera == "cam_d"
+        ][0]
+        self.assertEqual(cam_c[0], 0)
+        self.assertEqual(cam_d[0], cam_c[0] + cam_c[2])
+
+
+class TestBirdseyeActivity(unittest.TestCase):
+    """Test which camera activity is included in each Birdseye mode."""
+
+    def setUp(self):
+        config = {
+            "mqtt": {"enabled": False},
+            "birdseye": {
+                "enabled": True,
+                "mode": {
+                    "motion": True,
+                    "objects": True,
+                    "stationary_objects": True,
+                },
+            },
+            "cameras": {
+                "front": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
+                        ]
+                    },
+                    "detect": {"height": 1080, "width": 1920, "fps": 5},
+                }
+            },
+        }
+        self.manager = BirdsEyeFrameManager(FrigateConfig(**config), mp.Event())
+
+    def test_existing_modes_keep_their_activity_rules(self):
+        continuous = BirdseyeModeConfig(continuous=True)
+        motion = BirdseyeModeConfig(motion=True)
+        objects = BirdseyeModeConfig(objects=True)
+
+        no_activity = BirdseyeActivity(False, False, False)
+        motion_activity = BirdseyeActivity(False, False, True)
+        stationary_activity = BirdseyeActivity(False, True, False)
+        active_object_activity = BirdseyeActivity(True, False, False)
+
+        assert self.manager.camera_active(continuous, no_activity)
+        assert self.manager.camera_active(motion, motion_activity)
+        assert not self.manager.camera_active(motion, stationary_activity)
+        assert self.manager.camera_active(objects, active_object_activity)
+        assert not self.manager.camera_active(objects, stationary_activity)
+
+    def test_modes_can_be_combined(self):
+        mode = BirdseyeModeConfig(motion=True, stationary_objects=True)
+
+        assert self.manager.camera_active(mode, BirdseyeActivity(False, False, True))
+        assert self.manager.camera_active(mode, BirdseyeActivity(False, True, False))
+        assert not self.manager.camera_active(
+            mode, BirdseyeActivity(False, False, False)
+        )
+
+    def test_stationary_objects_are_independent_from_active_objects(self):
+        stationary_objects = BirdseyeModeConfig(stationary_objects=True)
+
+        assert self.manager.camera_active(
+            stationary_objects, BirdseyeActivity(False, True, False)
+        )
+        assert not self.manager.camera_active(
+            stationary_objects, BirdseyeActivity(True, False, False)
+        )
+
+    def test_write_data_preserves_active_and_confirms_stationary_activity(self):
+        birdseye = Birdseye.__new__(Birdseye)
+        birdseye.birdseye_manager = Mock()
+        birdseye.birdseye_manager.update.return_value = (False, False)
+        birdseye._idle_interval = None
+        frame = Mock()
+
+        birdseye.write_data(
+            "front",
+            [
+                {"stationary": True, "false_positive": True},
+                {"stationary": False, "false_positive": True},
+                {"stationary": True, "false_positive": False},
+            ],
+            [[0, 0, 10, 10]],
+            1.0,
+            frame,
+        )
+
+        birdseye.birdseye_manager.update.assert_called_once_with(
+            "front", BirdseyeActivity(True, True, True), 1.0, frame
+        )
+
+    def test_stationary_false_positive_does_not_activate_birdseye(self):
+        birdseye = Birdseye.__new__(Birdseye)
+        birdseye.birdseye_manager = Mock()
+        birdseye.birdseye_manager.update.return_value = (False, False)
+        birdseye._idle_interval = None
+        frame = Mock()
+
+        birdseye.write_data(
+            "front",
+            [{"stationary": True, "false_positive": True}],
+            [],
+            1.0,
+            frame,
+        )
+
+        birdseye.birdseye_manager.update.assert_called_once_with(
+            "front", BirdseyeActivity(False, False, False), 1.0, frame
+        )
+
 
 class TestBirdseyeCameraOrder(unittest.TestCase):
     """Test that birdseye reacts to camera order changes without a restart."""
@@ -55,7 +318,7 @@ class TestBirdseyeCameraOrder(unittest.TestCase):
     def setUp(self):
         config = {
             "mqtt": {"enabled": False},
-            "birdseye": {"enabled": True, "mode": "continuous"},
+            "birdseye": {"enabled": True, "mode": {"continuous": True}},
             "cameras": {
                 camera: {
                     "ffmpeg": {

frigate/test/test_config.py

@@ -7,9 +7,11 @@ import numpy as np
 from pydantic import ValidationError
 from ruamel.yaml.constructor import DuplicateKeyError
 
-from frigate.config import BirdseyeModeEnum, FrigateConfig
+from frigate.config import FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors import DetectorTypeEnum
+from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.device import build_detector_config, runner_names
 from frigate.util.builtin import deep_merge
 
 
@@ -64,49 +66,171 @@ class TestConfig(unittest.TestCase):
 
     def test_config_class(self):
         frigate_config = FrigateConfig(**self.minimal)
-        assert "cpu" in frigate_config.detectors.keys()
-        assert frigate_config.detectors["cpu"].type == DetectorTypeEnum.cpu
-        assert frigate_config.detectors["cpu"].model.width == 320
+        model = frigate_config.primary_model
+        assert model.scene == SceneEnum.all
+        assert model.width == 320
+        assert frigate_config.devices_for_model(model)[0].detector == (
+            DetectorTypeEnum.cpu
+        )
 
     @patch("frigate.detectors.detector_config.load_labels")
-    def test_detector_custom_model_path(self, mock_labels):
+    def test_model_custom_path(self, mock_labels):
         mock_labels.return_value = {}
         config = {
-            "detectors": {
-                "cpu": {
-                    "type": "cpu",
-                    "model_path": "/cpu_model.tflite",
+            "models": [
+                # needs to be a file that will exist, doesn't matter what
+                {"path": "/etc/hosts", "width": 512, "devices": ["openvino:GPU"]},
+            ],
+        }
+
+        frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+        model = frigate_config.primary_model
+
+        assert model.path == "/etc/hosts"
+        assert model.width == 512
+
+        detector_config = build_detector_config(
+            frigate_config.devices_for_model(model)[0], model
+        )
+        assert detector_config.type == DetectorTypeEnum.openvino
+        assert detector_config.device == "GPU"
+        assert detector_config.model.path == "/etc/hosts"
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_model_default_paths_per_detector(self, mock_labels):
+        mock_labels.return_value = {}
+
+        for devices, expected in (
+            (["cpu"], "/cpu_model.tflite"),
+            (["edgetpu:pci:0"], "/edgetpu_model.tflite"),
+            (["openvino:CPU"], "/openvino-model/ssdlite_mobilenet_v2.xml"),
+        ):
+            config = {"models": [{"devices": devices}]}
+            frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+            assert frigate_config.primary_model.path == expected
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_camera_picks_model_by_scene(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {
+            "models": [
+                {"scene": "outdoor", "devices": ["cpu"], "width": 320},
+                {"scene": "indoor", "devices": ["openvino:CPU"], "width": 300},
+            ],
+            "cameras": {
+                "back": {
+                    "detect": {"scene": "outdoor"},
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]},
+                        ]
+                    },
                 },
-                "edgetpu": {
-                    "type": "edgetpu",
-                    "model_path": "/edgetpu_model.tflite",
-                },
-                "openvino": {
-                    "type": "openvino",
+                "front": {
+                    "detect": {"scene": "indoor"},
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.2:554/video", "roles": ["detect"]},
+                        ]
+                    },
                 },
             },
-            # needs to be a file that will exist, doesn't matter what
-            "model": {"path": "/etc/hosts", "width": 512},
         }
 
         frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
 
-        assert "cpu" in frigate_config.detectors.keys()
-        assert "edgetpu" in frigate_config.detectors.keys()
-        assert "openvino" in frigate_config.detectors.keys()
+        assert frigate_config.model_for_camera("back").scene == SceneEnum.outdoor
+        assert frigate_config.model_for_camera("front").scene == SceneEnum.indoor
+        assert frigate_config.model_for_camera("back").width == 320
+        assert frigate_config.model_for_camera("front").width == 300
 
-        assert frigate_config.detectors["cpu"].type == DetectorTypeEnum.cpu
-        assert frigate_config.detectors["edgetpu"].type == DetectorTypeEnum.edgetpu
-        assert frigate_config.detectors["openvino"].type == DetectorTypeEnum.openvino
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_camera_requires_a_scene_without_a_default(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {
+            "models": [
+                {"scene": "outdoor", "devices": ["cpu"]},
+                {"scene": "indoor", "devices": ["openvino:CPU"]},
+            ],
+        }
 
-        assert frigate_config.detectors["cpu"].num_threads == 3
-        assert frigate_config.detectors["edgetpu"].device is None
-        assert frigate_config.detectors["openvino"].device is None
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
 
-        assert frigate_config.model.path == "/etc/hosts"
-        assert frigate_config.detectors["cpu"].model.path == "/cpu_model.tflite"
-        assert frigate_config.detectors["edgetpu"].model.path == "/edgetpu_model.tflite"
-        assert frigate_config.detectors["openvino"].model.path == "/etc/hosts"
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_camera_scene_must_match_a_model(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {
+            "models": [{"devices": ["cpu"]}],
+            "cameras": {
+                "back": {
+                    "detect": {"scene": "outdoor"},
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]},
+                        ]
+                    },
+                },
+            },
+        }
+
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_models_must_use_unique_scenes(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {
+            "models": [
+                {"scene": "outdoor", "devices": ["cpu"]},
+                {"scene": "outdoor", "devices": ["openvino:CPU"]},
+            ],
+        }
+
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_model_devices_must_share_a_detector(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {"models": [{"devices": ["cpu", "openvino:CPU"]}]}
+
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_model_requires_a_known_detector(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {"models": [{"devices": ["not_a_detector:0"]}]}
+
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_model_requires_a_device(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {"models": [{"devices": []}]}
+
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_shareable_devices_may_repeat(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {"models": [{"devices": ["openvino:GPU", "openvino:GPU"]}]}
+
+        frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
+        devices = frigate_config.devices_for_model(frigate_config.primary_model)
+
+        assert runner_names(devices) == ["openvino:GPU", "openvino:GPU#2"]
+
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_exclusive_devices_may_not_repeat(self, mock_labels):
+        mock_labels.return_value = {}
+        config = {"models": [{"devices": ["edgetpu:pci:0", "edgetpu:pci:0"]}]}
+
+        with self.assertRaises(ValidationError):
+            FrigateConfig(**(deep_merge(config, self.minimal)))
 
     def test_invalid_mqtt_config(self):
         config = {
@@ -170,7 +294,7 @@ class TestConfig(unittest.TestCase):
     def test_override_birdseye(self):
         config = {
             "mqtt": {"host": "mqtt"},
-            "birdseye": {"enabled": True, "mode": "continuous"},
+            "birdseye": {"enabled": True, "mode": {"continuous": True}},
             "cameras": {
                 "back": {
                     "ffmpeg": {
@@ -183,19 +307,30 @@ class TestConfig(unittest.TestCase):
                         "width": 1920,
                         "fps": 5,
                     },
-                    "birdseye": {"enabled": False, "mode": "motion"},
+                    "birdseye": {
+                        "enabled": False,
+                        "mode": {"continuous": False, "motion": True},
+                    },
                 }
             },
         }
 
         frigate_config = FrigateConfig(**config)
         assert not frigate_config.cameras["back"].birdseye.enabled
-        assert frigate_config.cameras["back"].birdseye.mode is BirdseyeModeEnum.motion
+        mode = frigate_config.cameras["back"].birdseye.mode
+        assert mode.motion
+        assert not mode.continuous
+        assert not mode.objects
+        assert not mode.stationary_objects
 
     def test_override_birdseye_non_inheritable(self):
         config = {
             "mqtt": {"host": "mqtt"},
-            "birdseye": {"enabled": True, "mode": "continuous", "height": 1920},
+            "birdseye": {
+                "enabled": True,
+                "mode": {"continuous": True},
+                "height": 1920,
+            },
             "cameras": {
                 "back": {
                     "ffmpeg": {
@@ -218,7 +353,7 @@ class TestConfig(unittest.TestCase):
     def test_inherit_birdseye(self):
         config = {
             "mqtt": {"host": "mqtt"},
-            "birdseye": {"enabled": True, "mode": "continuous"},
+            "birdseye": {"enabled": True, "mode": {"continuous": True}},
             "cameras": {
                 "back": {
                     "ffmpeg": {
@@ -237,9 +372,89 @@ class TestConfig(unittest.TestCase):
 
         frigate_config = FrigateConfig(**config)
         assert frigate_config.cameras["back"].birdseye.enabled
-        assert (
-            frigate_config.cameras["back"].birdseye.mode is BirdseyeModeEnum.continuous
-        )
+        mode = frigate_config.cameras["back"].birdseye.mode
+        assert mode.continuous
+        assert not mode.motion
+        assert not mode.objects
+        assert not mode.stationary_objects
+
+    def test_combine_birdseye_activity_types(self):
+        config = {
+            **self.minimal,
+            "birdseye": {
+                "mode": {
+                    "motion": True,
+                    "stationary_objects": True,
+                }
+            },
+        }
+
+        frigate_config = FrigateConfig(**config)
+        mode = frigate_config.cameras["back"].birdseye.mode
+        assert mode.motion
+        assert mode.stationary_objects
+        assert not mode.continuous
+        assert not mode.objects
+
+    def test_birdseye_requires_an_activity_type(self):
+        config = {
+            **self.minimal,
+            "birdseye": {
+                "mode": {
+                    "continuous": False,
+                    "motion": False,
+                    "objects": False,
+                    "stationary_objects": False,
+                }
+            },
+        }
+
+        with self.assertRaisesRegex(
+            ValidationError, "must enable at least one Birdseye activity type"
+        ):
+            FrigateConfig(**config)
+
+    def test_camera_can_disable_an_inherited_activity_type(self):
+        config = {
+            **self.minimal,
+            "birdseye": {"mode": {"motion": True, "objects": True}},
+        }
+        config["cameras"]["back"]["birdseye"] = {"mode": {"motion": False}}
+
+        frigate_config = FrigateConfig(**config)
+        mode = frigate_config.cameras["back"].birdseye.mode
+        assert not mode.motion
+        assert mode.objects
+
+    def test_profile_must_leave_an_activity_type_enabled(self):
+        config = {
+            **self.minimal,
+            "profiles": {"away": {"friendly_name": "Away"}},
+            "birdseye": {"mode": {"objects": True}},
+        }
+        config["cameras"]["back"]["profiles"] = {
+            "away": {"birdseye": {"mode": {"objects": False}}}
+        }
+
+        with self.assertRaisesRegex(
+            ValidationError, "must enable at least one Birdseye activity type"
+        ):
+            FrigateConfig(**config)
+
+    def test_camera_birdseye_activity_types_override_global_values(self):
+        config = {
+            **self.minimal,
+            "birdseye": {"mode": {"motion": True, "objects": True}},
+        }
+        config["cameras"]["back"]["birdseye"] = {
+            "mode": {"motion": False, "stationary_objects": True}
+        }
+
+        frigate_config = FrigateConfig(**config)
+        mode = frigate_config.cameras["back"].birdseye.mode
+        assert not mode.motion
+        assert mode.objects
+        assert mode.stationary_objects
 
     def test_override_tracked_objects(self):
         config = {
@@ -956,7 +1171,7 @@ class TestConfig(unittest.TestCase):
     def test_merge_labelmap(self):
         config = {
             "mqtt": {"host": "mqtt"},
-            "model": {"labelmap": {7: "truck"}},
+            "models": [{"labelmap": {7: "truck"}, "devices": ["cpu"]}],
             "cameras": {
                 "back": {
                     "ffmpeg": {
@@ -977,7 +1192,7 @@ class TestConfig(unittest.TestCase):
         }
 
         frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[7] == "truck"
+        assert frigate_config.primary_model.merged_labelmap[7] == "truck"
 
     def test_default_labelmap_empty(self):
         config = {
@@ -1002,12 +1217,12 @@ class TestConfig(unittest.TestCase):
         }
 
         frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[0] == "person"
+        assert frigate_config.primary_model.merged_labelmap[0] == "person"
 
     def test_default_labelmap(self):
         config = {
             "mqtt": {"host": "mqtt"},
-            "model": {"width": 320, "height": 320},
+            "models": [{"width": 320, "height": 320, "devices": ["cpu"]}],
             "cameras": {
                 "back": {
                     "ffmpeg": {
@@ -1028,7 +1243,7 @@ class TestConfig(unittest.TestCase):
         }
 
         frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[0] == "person"
+        assert frigate_config.primary_model.merged_labelmap[0] == "person"
 
     def test_plus_labelmap(self):
         with open(os.path.join(MODEL_CACHE_DIR, "test"), "w") as f:
@@ -1038,8 +1253,7 @@ class TestConfig(unittest.TestCase):
 
         config = {
             "mqtt": {"host": "mqtt"},
-            "detectors": {"cpu": {"type": "cpu"}},
-            "model": {"path": "plus://test"},
+            "models": [{"path": "plus://test", "devices": ["cpu"]}],
             "cameras": {
                 "back": {
                     "ffmpeg": {
@@ -1060,7 +1274,7 @@ class TestConfig(unittest.TestCase):
         }
 
         frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[0] == "amazon"
+        assert frigate_config.primary_model.merged_labelmap[0] == "amazon"
 
     def test_fails_on_invalid_role(self):
         config = {

frigate/test/test_config_migration.py

@@ -0,0 +1,225 @@
+"""Tests for migrating detectors and model into the models list."""
+
+import logging
+import os
+import tempfile
+import unittest
+from unittest.mock import patch
+
+from ruamel.yaml import YAML
+
+from frigate.util.config import (
+    CURRENT_CONFIG_VERSION,
+    migrate_frigate_config,
+    migrate_models,
+)
+
+
+class TestMigrateModels(unittest.TestCase):
+    def test_single_cpu_detector(self):
+        migrated = migrate_models({"detectors": {"cpu": {"type": "cpu"}}})
+
+        self.assertEqual(migrated["models"], [{"scene": "all", "devices": ["cpu"]}])
+        self.assertNotIn("detectors", migrated)
+
+    def test_model_settings_are_carried_over(self):
+        migrated = migrate_models(
+            {
+                "detectors": {"coral": {"type": "edgetpu", "device": "pci:0"}},
+                "model": {"path": "plus://abc", "width": 320},
+            }
+        )
+
+        self.assertEqual(
+            migrated["models"],
+            [
+                {
+                    "scene": "all",
+                    "path": "plus://abc",
+                    "width": 320,
+                    "devices": ["edgetpu:pci:0"],
+                }
+            ],
+        )
+        self.assertNotIn("model", migrated)
+
+    def test_multiple_corals_become_multiple_devices(self):
+        migrated = migrate_models(
+            {
+                "detectors": {
+                    "coral1": {"type": "edgetpu", "device": "pci:0"},
+                    "coral2": {"type": "edgetpu", "device": "pci:1"},
+                }
+            }
+        )
+
+        self.assertEqual(
+            migrated["models"][0]["devices"], ["edgetpu:pci:0", "edgetpu:pci:1"]
+        )
+
+    def test_several_detectors_on_one_device_stay_separate(self):
+        # a repeated device is now what running two inference processes on one
+        # piece of hardware looks like
+        migrated = migrate_models(
+            {
+                "detectors": {
+                    "ov_0": {"type": "openvino", "device": "GPU"},
+                    "ov_1": {"type": "openvino", "device": "GPU"},
+                }
+            }
+        )
+
+        self.assertEqual(
+            migrated["models"][0]["devices"], ["openvino:GPU", "openvino:GPU"]
+        )
+
+    def test_repeated_exclusive_devices_are_collapsed(self):
+        # two detectors both grabbing the first TPU was never really two TPUs
+        migrated = migrate_models(
+            {
+                "detectors": {
+                    "coral_0": {"type": "edgetpu", "device": "usb"},
+                    "coral_1": {"type": "edgetpu", "device": "usb"},
+                }
+            }
+        )
+
+        self.assertEqual(migrated["models"][0]["devices"], ["edgetpu:usb"])
+
+    def test_detectors_that_named_the_device_field_differently(self):
+        migrated = migrate_models(
+            {
+                "detectors": {
+                    "rk": {"type": "rknn", "num_cores": 2},
+                }
+            }
+        )
+
+        self.assertEqual(migrated["models"][0]["devices"], ["rknn:2"])
+
+    def test_empty_edgetpu_device_is_kept(self):
+        # an empty device selects a native Coral, which is not the same as
+        # letting the delegate pick
+        migrated = migrate_models(
+            {"detectors": {"coral": {"type": "edgetpu", "device": ""}}}
+        )
+
+        self.assertEqual(migrated["models"][0]["devices"], ["edgetpu:"])
+
+    def test_model_path_overrides_the_model(self):
+        migrated = migrate_models(
+            {
+                "detectors": {
+                    "coral": {"type": "edgetpu", "model_path": "/custom.tflite"}
+                },
+                "model": {"path": "/ignored.tflite", "width": 320},
+            }
+        )
+
+        self.assertEqual(migrated["models"][0]["path"], "/custom.tflite")
+
+    def test_no_detectors_falls_back_to_cpu(self):
+        migrated = migrate_models({"model": {"width": 320}})
+
+        self.assertEqual(migrated["models"][0]["devices"], ["cpu"])
+
+    def test_dropped_remote_detector_options_are_logged(self):
+        with self.assertLogs("frigate.util.config", level=logging.ERROR) as logs:
+            migrated = migrate_models(
+                {
+                    "detectors": {
+                        "ds": {
+                            "type": "deepstack",
+                            "api_url": "http://host:5000/v1/vision/detection",
+                            "api_key": "secret",
+                        }
+                    }
+                }
+            )
+
+        self.assertEqual(
+            migrated["models"][0]["devices"],
+            ["deepstack:http://host:5000/v1/vision/detection"],
+        )
+        self.assertTrue(any("api_key" in message for message in logs.output))
+
+    def test_mixed_detector_types_are_logged(self):
+        with self.assertLogs("frigate.util.config", level=logging.ERROR) as logs:
+            migrate_models(
+                {
+                    "detectors": {
+                        "ov": {"type": "openvino", "device": "GPU"},
+                        "coral": {"type": "edgetpu", "device": "pci:0"},
+                    }
+                }
+            )
+
+        self.assertTrue(any("more than one type" in message for message in logs.output))
+
+    def test_other_keys_are_untouched(self):
+        migrated = migrate_models(
+            {"mqtt": {"host": "mqtt"}, "detectors": {"cpu": {"type": "cpu"}}}
+        )
+
+        self.assertEqual(migrated["mqtt"], {"host": "mqtt"})
+
+
+class TestMigrateConfigFile(unittest.TestCase):
+    """The full file migration, which is gated on shape as well as version."""
+
+    def setUp(self):
+        self.temp_dir = tempfile.TemporaryDirectory()
+        self.addCleanup(self.temp_dir.cleanup)
+        self.config_file = os.path.join(self.temp_dir.name, "config.yml")
+        patcher = patch("frigate.util.config.CONFIG_DIR", self.temp_dir.name)
+        patcher.start()
+        self.addCleanup(patcher.stop)
+
+    def _migrate(self, config: str) -> dict:
+        with open(self.config_file, "w") as f:
+            f.write(config)
+
+        migrate_frigate_config(self.config_file)
+
+        with open(self.config_file) as f:
+            return YAML().load(f)
+
+    def test_migrates_a_config_already_stamped_with_the_current_version(self):
+        # 0.19 is unreleased, so a dev config can be current and still use
+        # the pre-models keys
+        migrated = self._migrate(
+            "mqtt:\n"
+            "  enabled: false\n"
+            "detectors:\n"
+            "  ov:\n"
+            "    type: openvino\n"
+            "    device: GPU\n"
+            "cameras: {}\n"
+            f"version: {CURRENT_CONFIG_VERSION}\n"
+        )
+
+        self.assertEqual(migrated["models"][0]["devices"], ["openvino:GPU"])
+        self.assertNotIn("detectors", migrated)
+
+    def test_a_migrated_config_is_left_alone(self):
+        migrated = self._migrate(
+            "mqtt:\n"
+            "  enabled: false\n"
+            "models:\n"
+            "  - scene: all\n"
+            "    devices:\n"
+            "      - openvino:GPU\n"
+            "cameras: {}\n"
+            f"version: {CURRENT_CONFIG_VERSION}\n"
+        )
+
+        self.assertEqual(
+            migrated["models"], [{"scene": "all", "devices": ["openvino:GPU"]}]
+        )
+        self.assertFalse(
+            os.path.exists(os.path.join(self.temp_dir.name, "backup_config.yaml"))
+        )
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

frigate/test/test_config_util.py

@@ -4,6 +4,7 @@ import unittest
 from unittest.mock import MagicMock
 
 from frigate.api.config_util import swap_runtime_config
+from frigate.config.holder import ConfigHolder
 
 
 class TestSwapRuntimeConfig(unittest.TestCase):
@@ -12,6 +13,7 @@ class TestSwapRuntimeConfig(unittest.TestCase):
     def _make_app(self) -> MagicMock:
         app = MagicMock()
         app.dispatcher.comms = [MagicMock(), MagicMock()]
+        app.config_holder = ConfigHolder(MagicMock(name="boot_config"))
         return app
 
     def test_rebinds_all_references(self) -> None:
@@ -37,11 +39,40 @@ class TestSwapRuntimeConfig(unittest.TestCase):
         # the swap rebuilds cameras from yaml, so overrides must be re-layered
         app.dispatcher.reapply_runtime_state_to_config.assert_called_once_with()
 
+    def test_updates_the_config_holder(self) -> None:
+        app = self._make_app()
+        holder = app.config_holder
+        config = MagicMock(name="new_config")
+
+        swap_runtime_config(app, config)
+
+        self.assertIs(holder.config, config)
+
+    def test_deferred_factory_builds_from_the_swapped_config(self) -> None:
+        """A watchdog-style factory must not rebuild from the boot config.
+
+        The factories in FrigateApp are lambdas evaluated when a process is
+        restarted, long after a user may have saved. Reading through the
+        holder is what keeps a rebuilt process from reverting every change
+        made since Frigate started.
+        """
+        app = self._make_app()
+        holder = app.config_holder
+        boot_config = holder.config
+        factory = lambda: holder.config  # noqa: E731
+        self.assertIs(factory(), boot_config)
+
+        config = MagicMock(name="new_config")
+        swap_runtime_config(app, config)
+
+        self.assertIs(factory(), config)
+
     def test_tolerates_missing_optional_collaborators(self) -> None:
         app = MagicMock()
         app.profile_manager = None
         app.stats_emitter = None
         app.dispatcher = None
+        app.config_holder = None
         config = MagicMock(name="new_config")
 
         # must not raise when the optional collaborators are absent

frigate/test/test_detector_device.py

@@ -0,0 +1,94 @@
+"""Tests for parsing detection hardware device strings."""
+
+import unittest
+
+from frigate.detectors.detector_config import ModelConfig
+from frigate.detectors.device import (
+    DeviceParseError,
+    build_detector_config,
+    parse_device,
+    runner_names,
+)
+
+
+class TestParseDevice(unittest.TestCase):
+    def test_bare_detector_has_no_device(self):
+        spec = parse_device("cpu")
+
+        self.assertEqual(spec.detector, "cpu")
+        self.assertIsNone(spec.device)
+
+    def test_device_is_everything_after_the_first_colon(self):
+        spec = parse_device("edgetpu:pci:0")
+
+        self.assertEqual(spec.detector, "edgetpu")
+        self.assertEqual(spec.device, "pci:0")
+
+    def test_trailing_colon_keeps_an_empty_device(self):
+        # an empty edgetpu device selects a native Coral
+        spec = parse_device("edgetpu:")
+
+        self.assertEqual(spec.detector, "edgetpu")
+        self.assertEqual(spec.device, "")
+
+    def test_unknown_detector_is_rejected(self):
+        with self.assertRaises(DeviceParseError):
+            parse_device("not_a_detector:0")
+
+    def test_device_that_the_detector_cannot_use_is_rejected(self):
+        # tensorrt takes a gpu index
+        with self.assertRaises(DeviceParseError):
+            parse_device("tensorrt:the-fast-one")
+
+
+class TestBuildDetectorConfig(unittest.TestCase):
+    def _build(self, raw: str):
+        return build_detector_config(parse_device(raw), ModelConfig())
+
+    def test_device_lands_on_the_detector_field(self):
+        for raw, expected in (
+            ("edgetpu:usb", "usb"),
+            ("edgetpu:pci:1", "pci:1"),
+            ("openvino:GPU.1", "GPU.1"),
+            ("onnx:CPU", "CPU"),
+            ("memryx:PCIe:0", "PCIe:0"),
+        ):
+            with self.subTest(raw=raw):
+                self.assertEqual(self._build(raw).device, expected)
+
+    def test_detectors_that_name_the_field_something_else(self):
+        self.assertEqual(self._build("cpu:4").num_threads, 4)
+        self.assertEqual(self._build("rknn:2").num_cores, 2)
+
+    def test_device_is_coerced_to_the_detector_field_type(self):
+        self.assertEqual(self._build("tensorrt:1").device, 1)
+
+    def test_omitted_device_falls_back_to_the_detector_default(self):
+        self.assertEqual(self._build("cpu").num_threads, 3)
+        self.assertEqual(self._build("rknn").num_cores, 0)
+        self.assertEqual(self._build("openvino").device, "AUTO")
+        self.assertIsNone(self._build("edgetpu").device)
+
+    def test_the_model_is_attached(self):
+        model = ModelConfig(path="/cpu_model.tflite")
+
+        self.assertIs(build_detector_config(parse_device("cpu"), model).model, model)
+
+
+class TestRunnerNames(unittest.TestCase):
+    def test_unique_devices_keep_their_name(self):
+        devices = [parse_device("edgetpu:pci:0"), parse_device("edgetpu:pci:1")]
+
+        self.assertEqual(runner_names(devices), ["edgetpu:pci:0", "edgetpu:pci:1"])
+
+    def test_repeated_devices_are_numbered(self):
+        devices = [parse_device("openvino:GPU")] * 3
+
+        self.assertEqual(
+            runner_names(devices),
+            ["openvino:GPU", "openvino:GPU#2", "openvino:GPU#3"],
+        )
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

frigate/test/test_dispatcher_runtime_state.py

@@ -5,8 +5,10 @@ import tempfile
 import unittest
 from unittest.mock import MagicMock, patch
 
+from frigate.app import FrigateApp
 from frigate.comms.dispatcher import Dispatcher
 from frigate.comms.runtime_state import RuntimeStatePersistence
+from frigate.config import BirdseyeModeConfig
 
 
 def _make_camera_mock(
@@ -50,6 +52,58 @@ def _build_dispatcher(cameras: dict[str, MagicMock]) -> Dispatcher:
         return Dispatcher(config, config_updater, onvif, ptz_metrics, communicators)
 
 
+class TestBirdseyeModeCommands(unittest.TestCase):
+    """Verify Birdseye mode commands use the boolean mode contract."""
+
+    def setUp(self) -> None:
+        self.camera = _make_camera_mock()
+        self.camera.birdseye.enabled = True
+        self.dispatcher = _build_dispatcher({"front_door": self.camera})
+        self.dispatcher.publish = MagicMock()
+
+    def test_combined_modes_are_accepted(self) -> None:
+        self.dispatcher._on_birdseye_mode_command(
+            "front_door", "STATIONARY_OBJECTS,MOTION"
+        )
+
+        self.assertEqual(
+            self.camera.birdseye.mode,
+            BirdseyeModeConfig(motion=True, stationary_objects=True),
+        )
+        self.dispatcher.config_updater.publish_update.assert_called_once()
+        self.dispatcher.publish.assert_called_once_with(
+            "front_door/birdseye_mode/state",
+            "MOTION,STATIONARY_OBJECTS",
+            retain=True,
+        )
+
+    def test_single_activity_type_is_accepted(self) -> None:
+        self.dispatcher._on_birdseye_mode_command("front_door", "OBJECTS")
+
+        self.assertEqual(
+            self.camera.birdseye.mode,
+            BirdseyeModeConfig(objects=True),
+        )
+        self.dispatcher.publish.assert_called_once_with(
+            "front_door/birdseye_mode/state", "OBJECTS", retain=True
+        )
+
+    def test_unknown_mode_is_rejected(self) -> None:
+        for payload in (
+            "UNKNOWN",
+            "motion",
+            "MOTION_OBJECTS",
+            "NONE",
+            "NONE,MOTION",
+            "MOTION,MOTION",
+            "MOTION,",
+        ):
+            with self.subTest(payload=payload):
+                self.dispatcher._on_birdseye_mode_command("front_door", payload)
+
+        self.dispatcher.config_updater.publish_update.assert_not_called()
+
+
 class TestRestoreRuntimeState(unittest.TestCase):
     """Verify replay routes through handlers and tolerates missing entries."""
 
@@ -363,5 +417,94 @@ class TestReapplyRuntimeStateToConfig(unittest.TestCase):
         dispatcher.reapply_runtime_state_to_config()
 
 
+class TestStartupAppliesConfigLayersBeforeWorkersStart(unittest.TestCase):
+    """Both layers must reach the config before config-carrying workers start.
+
+    A worker started before a layer is applied keeps the yaml value for the
+    rest of the session: the config_updater broadcast sent later is dropped
+    for subscribers that have not connected yet, and nothing re-sends it.
+    """
+
+    CONFIG_LAYERS = (
+        "profile_manager.restore_persisted_profile_to_config",
+        "dispatcher.reapply_runtime_state_to_config",
+    )
+
+    # started with a copy of the camera config
+    CONFIG_CARRYING_WORKERS = (
+        "start_video_output_processor",
+        "start_ptz_autotracker",
+        "start_detected_frames_processor",
+        "start_camera_processor",
+        "start_audio_processor",
+    )
+
+    def _start_call_order(self) -> list[str]:
+        """Return the names FrigateApp.start() calls, in order."""
+        app = MagicMock()
+
+        with (
+            patch("frigate.app.set_file_limit"),
+            patch("frigate.app.cleanup_replay_cameras"),
+            patch("frigate.app.reap_stale_exports"),
+            patch("frigate.app.create_fastapi_app"),
+            patch("frigate.app.uvicorn"),
+        ):
+            FrigateApp.start(app)
+
+        return [name for name, _, _ in app.mock_calls]
+
+    def test_applied_before_any_config_carrying_worker(self) -> None:
+        order = self._start_call_order()
+
+        for layer in self.CONFIG_LAYERS:
+            for worker in self.CONFIG_CARRYING_WORKERS:
+                self.assertLess(order.index(layer), order.index(worker))
+
+    def test_applied_after_the_dispatcher_exists(self) -> None:
+        order = self._start_call_order()
+
+        for layer in self.CONFIG_LAYERS:
+            self.assertLess(order.index("init_dispatcher"), order.index(layer))
+
+    def test_applied_after_the_profile_base_is_snapshotted(self) -> None:
+        # ProfileManager snapshots the config as the "no profile" base that
+        # deactivation resets to, so neither layer may be in the config yet
+        order = self._start_call_order()
+
+        for layer in self.CONFIG_LAYERS:
+            self.assertLess(order.index("init_profile_manager"), order.index(layer))
+
+    def test_layers_applied_in_order(self) -> None:
+        # a runtime toggle is the layer the user set last, so it goes on top
+        order = self._start_call_order()
+
+        self.assertLess(
+            order.index("profile_manager.restore_persisted_profile_to_config"),
+            order.index("dispatcher.reapply_runtime_state_to_config"),
+        )
+
+    def test_overrides_still_re_applied_after_the_profile_is_restored(self) -> None:
+        # activation resets the sections it owns to the base first, so the
+        # overrides have to land on top again
+        order = self._start_call_order()
+
+        self.assertLess(
+            order.index("profile_manager.restore_persisted_profile"),
+            order.index("dispatcher.restore_runtime_state"),
+        )
+
+    def test_broadcast_replay_still_runs_at_the_end(self) -> None:
+        # the broadcast is the only channel for the recording, review, and
+        # embeddings processes, which start before the config can be corrected
+        order = self._start_call_order()
+
+        for replay in (
+            "profile_manager.restore_persisted_profile",
+            "dispatcher.restore_runtime_state",
+        ):
+            self.assertLess(order.index("start_audio_processor"), order.index(replay))
+
+
 if __name__ == "__main__":
     unittest.main()

frigate/test/test_genai_processor_sync.py

@@ -0,0 +1,213 @@
+"""Tests for GenAI enablement gating in the embeddings maintainer.
+
+Covers creating post processors when GenAI is enabled at runtime, and the
+per-camera gating those processors apply once they exist.
+"""
+
+import sys
+import unittest
+from unittest.mock import MagicMock, patch
+
+# Mock TFLite before importing the maintainer
+_MOCK_MODULES = [
+    "tflite_runtime",
+    "tflite_runtime.interpreter",
+    "ai_edge_litert",
+    "ai_edge_litert.interpreter",
+]
+for mod in _MOCK_MODULES:
+    if mod not in sys.modules:
+        sys.modules[mod] = MagicMock()
+
+# imported from the maintainer to avoid tripping the circular import between
+# the maintainer and the processor modules
+from frigate.embeddings.maintainer import (  # noqa: E402
+    EmbeddingMaintainer,
+    ObjectDescriptionProcessor,
+    PostProcessDataEnum,
+    ReviewDescriptionProcessor,
+)
+
+
+class TestGenAIProcessorSync(unittest.TestCase):
+    """Enabling GenAI on the first camera must not require a restart."""
+
+    def _make_maintainer(
+        self,
+        review: bool = False,
+        objects: bool = False,
+        review_in_config: bool | None = None,
+        objects_in_config: bool | None = None,
+    ) -> EmbeddingMaintainer:
+        # Bypass the heavy __init__; only the attributes touched by
+        # _sync_genai_processors are needed for these tests.
+        maintainer = EmbeddingMaintainer.__new__(EmbeddingMaintainer)
+        maintainer.post_processors = []
+        maintainer.config = MagicMock()
+        maintainer.config.cameras = {
+            "front": self._make_camera(
+                review,
+                objects,
+                review if review_in_config is None else review_in_config,
+                objects if objects_in_config is None else objects_in_config,
+            )
+        }
+        maintainer.config_updater = MagicMock()
+        maintainer.embeddings = None
+        maintainer.requestor = MagicMock()
+        maintainer.metrics = MagicMock()
+        maintainer.genai_manager = MagicMock()
+        maintainer.semantic_trigger_processor = None
+        return maintainer
+
+    def _make_camera(
+        self,
+        review: bool,
+        objects: bool,
+        review_in_config: bool,
+        objects_in_config: bool,
+    ) -> MagicMock:
+        camera = MagicMock()
+        camera.review.genai.enabled = review
+        camera.review.genai.enabled_in_config = review_in_config
+        camera.objects.genai.enabled = objects
+        camera.objects.genai.enabled_in_config = objects_in_config
+        return camera
+
+    def _processor_types(self, maintainer: EmbeddingMaintainer) -> list[type]:
+        return [type(p) for p in maintainer.post_processors]
+
+    def test_no_processors_when_genai_disabled(self):
+        """A config with no GenAI cameras registers neither processor."""
+        maintainer = self._make_maintainer()
+
+        maintainer._sync_genai_processors()
+
+        self.assertEqual(maintainer.post_processors, [])
+
+    def test_review_processor_added_when_enabled_after_startup(self):
+        """Enabling review GenAI on the first camera registers the processor."""
+        maintainer = self._make_maintainer()
+        maintainer._sync_genai_processors()
+
+        camera = maintainer.config.cameras["front"]
+        camera.review.genai.enabled = True
+        camera.review.genai.enabled_in_config = True
+        maintainer._sync_genai_processors()
+
+        self.assertEqual(
+            self._processor_types(maintainer), [ReviewDescriptionProcessor]
+        )
+
+    def test_object_processor_added_when_enabled_after_startup(self):
+        """Enabling object GenAI on the first camera registers the processor."""
+        maintainer = self._make_maintainer()
+        maintainer._sync_genai_processors()
+
+        camera = maintainer.config.cameras["front"]
+        camera.objects.genai.enabled = True
+        camera.objects.genai.enabled_in_config = True
+        maintainer._sync_genai_processors()
+
+        self.assertEqual(
+            self._processor_types(maintainer), [ObjectDescriptionProcessor]
+        )
+
+    def test_processor_added_when_only_enabled_by_profile(self):
+        """A profile enables GenAI without setting enabled_in_config."""
+        maintainer = self._make_maintainer(
+            review=True, objects=True, review_in_config=False, objects_in_config=False
+        )
+
+        maintainer._sync_genai_processors()
+
+        self.assertEqual(
+            self._processor_types(maintainer),
+            [ReviewDescriptionProcessor, ObjectDescriptionProcessor],
+        )
+
+    def test_processors_are_not_duplicated(self):
+        """Repeated config updates must not register a second processor."""
+        maintainer = self._make_maintainer(review=True, objects=True)
+
+        maintainer._sync_genai_processors()
+        maintainer._sync_genai_processors()
+
+        self.assertEqual(
+            self._processor_types(maintainer),
+            [ReviewDescriptionProcessor, ObjectDescriptionProcessor],
+        )
+
+    def test_genai_topic_triggers_sync(self):
+        """A camera config update on a GenAI topic registers the processor."""
+        maintainer = self._make_maintainer(review=True)
+        maintainer.config_updater.check_for_updates.return_value = {"review": ["front"]}
+
+        maintainer._check_camera_config_updates()
+
+        self.assertEqual(
+            self._processor_types(maintainer), [ReviewDescriptionProcessor]
+        )
+
+    def test_unrelated_topic_does_not_sync(self):
+        """An unrelated camera config update must not register processors."""
+        maintainer = self._make_maintainer(review=True)
+        maintainer.config_updater.check_for_updates.return_value = {"motion": ["front"]}
+
+        maintainer._check_camera_config_updates()
+
+        self.assertEqual(maintainer.post_processors, [])
+
+
+class TestObjectDescriptionCameraGating(unittest.TestCase):
+    """One camera enabling object descriptions must not enlist the others."""
+
+    def _make_processor(self, enabled: bool) -> ObjectDescriptionProcessor:
+        config = MagicMock()
+        camera = MagicMock()
+        camera.objects.genai.enabled = enabled
+        camera.objects.genai.send_triggers.after_significant_updates = None
+        config.cameras = {"front": camera}
+
+        genai_manager = MagicMock()
+        genai_manager.description_client = MagicMock()
+
+        return ObjectDescriptionProcessor(
+            config, None, MagicMock(), MagicMock(), genai_manager, None
+        )
+
+    def _update(self, processor: ObjectDescriptionProcessor) -> None:
+        processor.process_data(
+            {
+                "camera": "front",
+                "data": {
+                    "id": "1234.5-abcdef",
+                    "box": (0, 0, 10, 10),
+                    "stationary": False,
+                },
+                "state": "update",
+                "yuv_frame": MagicMock(),
+            },
+            PostProcessDataEnum.tracked_object,
+        )
+
+    @patch("frigate.data_processing.post.object_descriptions.create_thumbnail")
+    def test_disabled_camera_collects_no_thumbnails(self, mock_create_thumbnail):
+        """A camera with object descriptions off does no thumbnail work."""
+        processor = self._make_processor(enabled=False)
+
+        self._update(processor)
+
+        mock_create_thumbnail.assert_not_called()
+        self.assertEqual(processor.tracked_events, {})
+
+    @patch("frigate.data_processing.post.object_descriptions.create_thumbnail")
+    def test_enabled_camera_collects_thumbnails(self, mock_create_thumbnail):
+        """A camera with object descriptions on still collects thumbnails."""
+        mock_create_thumbnail.return_value = b"jpg"
+        processor = self._make_processor(enabled=True)
+
+        self._update(processor)
+
+        mock_create_thumbnail.assert_called_once()
+        self.assertEqual(len(processor.tracked_events["1234.5-abcdef"]), 1)

frigate/test/test_gpu_stats.py

@@ -1,7 +1,12 @@
 import unittest
+from io import StringIO
 from unittest.mock import MagicMock, patch
 
-from frigate.util.services import get_amd_gpu_stats, get_intel_gpu_stats
+from frigate.util.services import (
+    get_amd_gpu_stats,
+    get_intel_gpu_stats,
+    get_openvino_npu_stats,
+)
 
 
 class TestGpuStats(unittest.TestCase):
@@ -17,6 +22,88 @@ class TestGpuStats(unittest.TestCase):
         amd_stats = get_amd_gpu_stats()
         assert amd_stats == {"gpu": "4.17%", "mem": "60.37%"}
 
+    @patch("frigate.util.services.time.sleep")
+    @patch("frigate.util.services.time.time", side_effect=[0.0, 1.0])
+    @patch(
+        "frigate.util.services.os.readlink",
+        return_value="/sys/bus/pci/drivers/intel_vpu",
+    )
+    @patch(
+        "frigate.util.services.glob.glob",
+        return_value=["/sys/class/accel/accel0"],
+    )
+    @patch(
+        "builtins.open",
+        side_effect=[StringIO("1000"), StringIO("1250")],
+    )
+    def test_openvino_npu_stats_discovers_accel0(
+        self, open_file, glob, readlink, time, sleep
+    ):
+        assert get_openvino_npu_stats() == {"npu": "25.0", "mem": "-%"}
+
+        open_file.assert_any_call(
+            "/sys/class/accel/accel0/device/power/runtime_active_time"
+        )
+
+    @patch("frigate.util.services.time.sleep")
+    @patch("frigate.util.services.time.time", side_effect=[0.0, 1.0])
+    @patch(
+        "frigate.util.services.os.readlink",
+        side_effect=[
+            "/sys/bus/pci/drivers/other",
+            "/sys/bus/pci/drivers/intel_vpu",
+        ],
+    )
+    @patch(
+        "frigate.util.services.glob.glob",
+        return_value=[
+            "/sys/class/accel/accel0",
+            "/sys/class/accel/accel1",
+        ],
+    )
+    @patch(
+        "builtins.open",
+        side_effect=[StringIO("1000"), StringIO("1250")],
+    )
+    def test_openvino_npu_stats_skips_non_intel_accelerator(
+        self, open_file, glob, readlink, time, sleep
+    ):
+        assert get_openvino_npu_stats() == {"npu": "25.0", "mem": "-%"}
+
+        open_file.assert_any_call(
+            "/sys/class/accel/accel1/device/power/runtime_active_time"
+        )
+
+    @patch(
+        "frigate.util.services.os.readlink",
+        return_value="/sys/bus/pci/drivers/other",
+    )
+    @patch(
+        "frigate.util.services.glob.glob",
+        return_value=["/sys/class/accel/accel0"],
+    )
+    @patch("builtins.open")
+    def test_openvino_npu_stats_no_intel_accelerator(self, open_file, glob, readlink):
+        assert get_openvino_npu_stats() is None
+        open_file.assert_not_called()
+
+    @patch(
+        "frigate.util.services.os.readlink",
+        return_value="/sys/bus/pci/drivers/intel_vpu",
+    )
+    @patch(
+        "frigate.util.services.glob.glob",
+        return_value=["/sys/class/accel/accel0"],
+    )
+    @patch("builtins.open", side_effect=FileNotFoundError)
+    def test_openvino_npu_stats_runtime_counter_unavailable(
+        self, open_file, glob, readlink
+    ):
+        assert get_openvino_npu_stats() is None
+        open_file.assert_called_once_with(
+            "/sys/class/accel/accel0/device/power/runtime_active_time"
+        )
+
     @patch("frigate.stats.intel_gpu_info.intel_gpu_name_resolver.get_names")
     @patch("frigate.util.services.time.sleep")
     @patch("frigate.util.services.time.monotonic")

frigate/test/test_network_config.py

@@ -0,0 +1,41 @@
+"""Tests for networking config validation."""
+
+import unittest
+
+from pydantic import ValidationError
+
+from frigate.config.network import ListenConfig
+
+
+class TestListenConfig(unittest.TestCase):
+    def test_defaults_are_distinct(self):
+        listen = ListenConfig()
+
+        self.assertEqual(listen.internal_port, 5000)
+        self.assertEqual(listen.external_port, 8971)
+
+    def test_address_and_port_string_is_parsed(self):
+        listen = ListenConfig(internal="127.0.0.1:5000", external="0.0.0.0:8971")
+
+        self.assertEqual(listen.internal_port, 5000)
+        self.assertEqual(listen.external_port, 8971)
+
+    def test_identical_ports_rejected(self):
+        with self.assertRaises(ValidationError):
+            ListenConfig(internal=8971, external=8971)
+
+    def test_same_port_on_different_addresses_rejected(self):
+        # nginx would accept these as distinct listeners, but /auth decides on
+        # the port alone, so the external one would inherit anonymous admin
+        with self.assertRaises(ValidationError):
+            ListenConfig(internal="127.0.0.1:8971", external="0.0.0.0:8971")
+
+    def test_distinct_ports_accepted(self):
+        listen = ListenConfig(internal=5001, external="0.0.0.0:8971")
+
+        self.assertEqual(listen.internal_port, 5001)
+        self.assertEqual(listen.external_port, 8971)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

frigate/test/test_post_processing.py

@@ -0,0 +1,226 @@
+"""Tests for detector post-processing NMS box format handling.
+
+cv2.dnn.NMSBoxes expects boxes as [x, y, width, height]. Passing corner
+coordinates [x1, y1, x2, y2] makes OpenCV treat x2/y2 as width/height,
+inflating every box toward the bottom-right by its distance from the origin.
+Two well separated objects far from the origin then appear to overlap and the
+lower scoring one is silently suppressed.
+
+The regression geometry used throughout: two boxes with zero true overlap,
+A = (393, 499, 484, 620) and B = (527, 499, 618, 620) in a 640x640 input
+(43 px gap). Misread as [x, y, w, h] their IoU is 0.465, above the 0.4 NMS
+threshold, so the buggy format drops the lower scoring box while correct
+conversion keeps both.
+"""
+
+import math
+import unittest
+from queue import Queue
+
+import numpy as np
+
+from frigate.detectors.plugins.memryx import MemryXDetector
+from frigate.util.model import (
+    post_process_dfine,
+    post_process_rfdetr,
+    post_process_yolo,
+    post_process_yolox,
+)
+
+WIDTH = 640
+HEIGHT = 640
+
+# box A: xyxy (393, 499, 484, 620) as center format
+A_CX, A_CY, A_W, A_H = 438.5, 559.5, 91.0, 121.0
+# box B: xyxy (527, 499, 618, 620) as center format
+B_CX, B_CY, B_W, B_H = 572.5, 559.5, 91.0, 121.0
+
+# expected normalized output rows: [class_id, conf, y1, x1, y2, x2]
+A_ROW = [499 / 640, 393 / 640, 620 / 640, 484 / 640]
+B_ROW = [499 / 640, 527 / 640, 620 / 640, 618 / 640]
+
+
+def kept(detections: np.ndarray) -> np.ndarray:
+    """Rows of the padded (20, 6) output that hold real detections."""
+    return detections[detections[:, 1] > 0]
+
+
+class TestYoloNmsPostProcess(unittest.TestCase):
+    def _single_output(self, rows: list[list[float]]) -> list[np.ndarray]:
+        """Build a single-tensor YOLO output (1, attrs, anchors) from
+        [cx, cy, w, h, class scores...] rows, padded with empty anchors."""
+        anchors = np.zeros((10, len(rows[0])), dtype=np.float32)
+        anchors[: len(rows)] = np.array(rows, dtype=np.float32)
+        return [anchors.T[np.newaxis, ...]]
+
+    def test_keeps_separated_objects_far_from_origin(self):
+        output = self._single_output(
+            [
+                [A_CX, A_CY, A_W, A_H, 0.90, 0.0],
+                [B_CX, B_CY, B_W, B_H, 0.0, 0.85],
+            ]
+        )
+
+        detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
+
+    def test_still_suppresses_true_duplicates(self):
+        # same object twice, shifted 4 px: true IoU 0.92, must dedupe to one
+        output = self._single_output(
+            [
+                [A_CX, A_CY, A_W, A_H, 0.90, 0.0],
+                [A_CX + 4, A_CY, A_W, A_H, 0.85, 0.0],
+            ]
+        )
+
+        detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 1)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+
+
+class TestMultipartYoloPostProcess(unittest.TestCase):
+    def _multipart_output(self) -> list[np.ndarray]:
+        """Build a 3-scale anchor-based YOLO output containing boxes A and B,
+        both decoded through anchor 0 of the stride-32 scale."""
+        outputs = [
+            np.zeros((1, 255, 80, 80), dtype=np.float32),
+            np.zeros((1, 255, 40, 40), dtype=np.float32),
+            np.zeros((1, 255, 20, 20), dtype=np.float32),
+        ]
+        stride, (anchor_w, anchor_h) = 32, (142, 110)
+
+        for cx, cy, w, h, conf, class_channel in [
+            (A_CX, A_CY, A_W, A_H, 0.95, 5),  # class 0
+            (B_CX, B_CY, B_W, B_H, 0.90, 6),  # class 1
+        ]:
+            cell_x, cell_y = int(cx // stride), int(cy // stride)
+            dx = (cx / stride - cell_x + 0.5) / 2
+            dy = (cy / stride - cell_y + 0.5) / 2
+            dw = math.sqrt(w / anchor_w) / 2
+            dh = math.sqrt(h / anchor_h) / 2
+            # anchor 0 occupies channels 0-84 of the 255 channel tensor
+            outputs[2][0, 0:4, cell_y, cell_x] = [dx, dy, dw, dh]
+            outputs[2][0, 4, cell_y, cell_x] = conf
+            outputs[2][0, class_channel, cell_y, cell_x] = 1.0
+
+        return outputs
+
+    def test_keeps_separated_objects_far_from_origin(self):
+        detections = kept(post_process_yolo(self._multipart_output(), WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.95, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.90, *B_ROW], atol=2e-3)
+
+    def test_empty_output_returns_no_detections(self):
+        outputs = [
+            np.zeros((1, 255, 80, 80), dtype=np.float32),
+            np.zeros((1, 255, 40, 40), dtype=np.float32),
+            np.zeros((1, 255, 20, 20), dtype=np.float32),
+        ]
+
+        detections = kept(post_process_yolo(outputs, WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 0)
+
+
+class TestYoloxPostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # with zero grids and unit strides the decode reduces to
+        # cx = raw cx and w = exp(raw w)
+        rows = np.zeros((10, 7), dtype=np.float32)
+        rows[0] = [A_CX, A_CY, math.log(A_W), math.log(A_H), 1.0, 0.90, 0.0]
+        rows[1] = [B_CX, B_CY, math.log(B_W), math.log(B_H), 1.0, 0.0, 0.85]
+        predictions = rows[np.newaxis, ...]
+        grids = np.zeros((1, 10, 2), dtype=np.float32)
+        expanded_strides = np.ones((1, 10, 1), dtype=np.float32)
+
+        detections = kept(
+            post_process_yolox(predictions, WIDTH, HEIGHT, grids, expanded_strides)
+        )
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
+
+
+class TestDfinePostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # D-FINE emits absolute pixel xyxy boxes alongside labels and scores
+        labels = np.zeros((1, 10), dtype=np.int64)
+        labels[0, 1] = 1
+        boxes = np.zeros((1, 10, 4), dtype=np.float32)
+        boxes[0, 0] = [393, 499, 484, 620]
+        boxes[0, 1] = [527, 499, 618, 620]
+        scores = np.zeros((1, 10), dtype=np.float32)
+        scores[0, 0] = 0.90
+        scores[0, 1] = 0.85
+
+        detections = kept(post_process_dfine([labels, boxes, scores], WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
+
+
+class TestRfdetrPostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # RF-DETR emits normalized center format boxes and class logits where
+        # logit index 0 is the background class
+        boxes = np.zeros((1, 10, 4), dtype=np.float32)
+        boxes[0, 0] = [A_CX / WIDTH, A_CY / HEIGHT, A_W / WIDTH, A_H / HEIGHT]
+        boxes[0, 1] = [B_CX / WIDTH, B_CY / HEIGHT, B_W / WIDTH, B_H / HEIGHT]
+        # background heavy logits everywhere, then two confident objects
+        logits = np.tile(np.array([10.0, 0.0, 0.0], dtype=np.float32), (1, 10, 1))
+        logits[0, 0] = [0.0, 4.0, 0.0]  # class 0 after background offset
+        logits[0, 1] = [0.0, 0.0, 3.5]  # class 1 after background offset
+
+        detections = kept(post_process_rfdetr([boxes, logits]))
+
+        conf_a = math.exp(4.0) / (math.exp(4.0) + 2)
+        conf_b = math.exp(3.5) / (math.exp(3.5) + 2)
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, conf_a, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, conf_b, *B_ROW], atol=2e-3)
+
+
+class TestMemryxSsdlitePostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # the NMS math runs on the host CPU, so the real method is testable
+        # without MemryX hardware; it only needs the model dimensions and
+        # the output queue
+        detector = object.__new__(MemryXDetector)
+        detector.memx_model_width = WIDTH
+        detector.memx_model_height = HEIGHT
+        detector.output_queue = Queue()
+
+        # this path uses a 0.5 NMS threshold, so use a tighter pair: zero
+        # true overlap (10 px gap), IoU 0.69 when misread as [x, y, w, h]
+        dets = np.zeros((1, 10, 5), dtype=np.float32)
+        dets[0, 0] = [480, 500, 540, 620, 0.90]
+        dets[0, 1] = [550, 500, 610, 620, 0.85]
+        labels = np.zeros((1, 10), dtype=np.float32)
+        labels[0, 1] = 1
+
+        detector.post_process_ssdlite([dets, labels])
+        detections = kept(detector.output_queue.get())
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(
+            detections[0],
+            [0, 0.90, 500 / 640, 480 / 640, 620 / 640, 540 / 640],
+            atol=2e-3,
+        )
+        np.testing.assert_allclose(
+            detections[1],
+            [1, 0.85, 500 / 640, 550 / 640, 620 / 640, 610 / 640],
+            atol=2e-3,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

frigate/test/test_profiles.py

@@ -560,6 +560,25 @@ class TestProfileManager(unittest.TestCase):
         assert err is None
         assert self.config.cameras["front"].enabled is False
 
+    @patch.object(ProfileManager, "_persist_active_profile")
+    def test_profile_can_disable_inherited_birdseye_activity(self, mock_persist):
+        """A false-only mode override inherits the other base activity types."""
+        self.config.profiles["away"] = ProfileDefinitionConfig(friendly_name="Away")
+        base_mode = self.config.cameras["front"].birdseye.mode
+        base_mode.motion = True
+        base_mode.objects = True
+        self.config.cameras["front"].profiles["away"] = CameraProfileConfig(
+            birdseye={"mode": {"motion": False}}
+        )
+        self.manager = ProfileManager(self.config, self.mock_updater)
+
+        err = self.manager.activate_profile("away")
+
+        assert err is None
+        mode = self.config.cameras["front"].birdseye.mode
+        assert not mode.motion
+        assert mode.objects
+
     @patch.object(ProfileManager, "_persist_active_profile")
     def test_deactivate_restores_enabled(self, mock_persist):
         """Deactivating a profile restores the camera's base enabled state."""
@@ -785,6 +804,98 @@ class TestProfileManager(unittest.TestCase):
         manager.activate_profile("armed", clear_runtime_overrides=False)
         dispatcher.clear_runtime_state.assert_not_called()
 
+    def test_apply_profile_to_config_mutates_the_config(self):
+        """The config-only half applies the same overrides as activation."""
+        err = self.manager.apply_profile_to_config("armed")
+        assert err is None
+
+        front = self.config.cameras["front"]
+        assert front.notifications.enabled is True
+        assert front.objects.track == ["person", "car", "package"]
+
+    def test_apply_profile_to_config_makes_no_zmq_mqtt_or_disk_writes(self):
+        """Workers are started with the values, so nothing is published yet."""
+        dispatcher = MagicMock()
+        manager = ProfileManager(self.config, self.mock_updater, dispatcher)
+
+        with patch.object(ProfileManager, "_persist_active_profile") as mock_persist:
+            manager.apply_profile_to_config("armed")
+
+        self.mock_updater.publish_update.assert_not_called()
+        dispatcher.publish.assert_not_called()
+        mock_persist.assert_not_called()
+        # bookkeeping stays with activate_profile
+        assert self.config.active_profile is None
+
+    def test_apply_profile_to_config_rejects_an_unknown_profile(self):
+        err = self.manager.apply_profile_to_config("nonexistent")
+        assert err is not None
+        assert "not defined" in err
+
+    def test_restore_persisted_profile_to_config_applies_it(self):
+        """The startup config pass restores what was persisted."""
+        with patch.object(
+            ProfileManager, "load_persisted_profile", return_value="armed"
+        ):
+            self.manager.restore_persisted_profile_to_config()
+
+        assert self.config.cameras["front"].notifications.enabled is True
+        # still the config-only half, so nothing is published or persisted
+        self.mock_updater.publish_update.assert_not_called()
+        assert self.config.active_profile is None
+
+    def test_restore_persisted_profile_to_config_no_op_when_none_persisted(self):
+        with patch.object(ProfileManager, "load_persisted_profile", return_value=None):
+            self.manager.restore_persisted_profile_to_config()
+
+        assert self.config.cameras["front"].notifications.enabled is False
+
+    def test_restore_persisted_profile_to_config_ignores_a_stale_name(self):
+        """A profile no longer offered by any camera must not be applied."""
+        with patch.object(
+            ProfileManager, "load_persisted_profile", return_value="ghost"
+        ):
+            self.manager.restore_persisted_profile_to_config()
+
+        assert self.config.cameras["front"].notifications.enabled is False
+
+    @patch.object(ProfileManager, "_persist_active_profile")
+    def test_restore_persisted_profile_activates_and_publishes(self, mock_persist):
+        """The startup publish pass runs a full activation."""
+        dispatcher = MagicMock()
+        manager = ProfileManager(self.config, self.mock_updater, dispatcher)
+
+        with patch.object(
+            ProfileManager, "load_persisted_profile", return_value="armed"
+        ):
+            manager.restore_persisted_profile()
+
+        assert self.config.active_profile == "armed"
+        self.mock_updater.publish_update.assert_called()
+        # a startup replay must not wipe the runtime overrides layered on top
+        dispatcher.clear_runtime_state.assert_not_called()
+
+    @patch.object(ProfileManager, "_persist_active_profile")
+    def test_activation_after_apply_still_publishes_every_section(self, mock_persist):
+        """Re-deriving the same state must not skip the broadcast.
+
+        The processes that started before the config was corrected have no
+        other channel.
+        """
+        self.manager.apply_profile_to_config("armed")
+        self.mock_updater.publish_update.reset_mock()
+
+        err = self.manager.activate_profile("armed", clear_runtime_overrides=False)
+        assert err is None
+
+        published = {
+            call.args[0].update_type.name
+            for call in self.mock_updater.publish_update.call_args_list
+        }
+        assert "notifications" in published
+        assert "objects" in published
+        assert self.config.active_profile == "armed"
+
     @patch.object(ProfileManager, "_persist_active_profile")
     def test_update_config_preserves_runtime_state_with_active_profile(
         self, mock_persist

frigate/test/test_ptz_onvif.py

@@ -1,4 +1,4 @@
-"""Tests for ONVIF init state that must not depend on the autotracking config.
+"""Tests for ONVIF state that must not depend on the autotracking config.
 
 Regression coverage for a camera that is initialized while autotracking is off and
 has it enabled later, which is the normal wizard flow: set the camera up first,
@@ -10,12 +10,17 @@ the tracking thread.
 
 The request objects are built from the locally parsed WSDL and cost no network, so
 they are always created and init=True now implies they exist.
+
+Also covers the inverse direction: the ptz movement timestamps must not be written
+for a camera that has autotracking off, because nothing clears them back out.
 """
 
 import unittest
 from unittest.mock import AsyncMock, MagicMock
 
+from frigate.camera import PTZMetrics
 from frigate.config import FrigateConfig
+from frigate.ptz.autotrack import ptz_moving_at_frame_time
 from frigate.ptz.onvif import OnvifController
 
 CAMERA = "ptz_cam"
@@ -97,6 +102,36 @@ def _make_controller(autotracking_enabled: bool) -> OnvifController:
     return controller
 
 
+def _make_move_controller(autotracking_enabled: bool) -> OnvifController:
+    """Build an already initialized controller for a camera that supports relative
+    FOV movement, with real metrics so the timestamp writes can be asserted on."""
+    config = _config(autotracking_enabled)
+    controller = OnvifController.__new__(OnvifController)
+    controller.config = config
+    controller.camera_configs = {CAMERA: config.cameras[CAMERA]}
+    controller.failed_cams = {}
+
+    ptz = MagicMock()
+    ptz.RelativeMove = AsyncMock()
+    controller.cams = {
+        CAMERA: {
+            "init": True,
+            "active": False,
+            "ptz": ptz,
+            "features": ["pt", "pt-r-fov"],
+            "relative_move_request": MagicMock(),
+            "relative_fov_range": {
+                "XRange": {"Min": -1.0, "Max": 1.0},
+                "YRange": {"Min": -1.0, "Max": 1.0},
+            },
+        }
+    }
+    controller.ptz_metrics = {
+        CAMERA: PTZMetrics(autotracker_enabled=autotracking_enabled)
+    }
+    return controller
+
+
 class TestOnvifInitRequests(unittest.IsolatedAsyncioTestCase):
     async def test_status_request_created_when_autotracking_disabled(self) -> None:
         # the wizard flow: onvif configured first, autotracking enabled later
@@ -143,5 +178,61 @@ class TestOnvifInitRequests(unittest.IsolatedAsyncioTestCase):
         ptz.GetStatus.assert_not_called()
 
 
+class TestManualRelativeMoveMetrics(unittest.IsolatedAsyncioTestCase):
+    """A manual move from the UI (click to move, drag to zoom) sends move_relative
+    for any camera that advertises pt-r-fov, autotracking or not."""
+
+    async def test_metrics_untouched_when_autotracking_disabled(self) -> None:
+        # only camera_maintenance polls get_camera_status, and only for autotracking
+        # cameras, so a manual move that starts the clock here is never stopped
+        controller = _make_move_controller(autotracking_enabled=False)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        controller.cams[CAMERA]["ptz"].RelativeMove.assert_awaited_once()
+        self.assertEqual(metrics.start_time.value, 0)
+        self.assertEqual(metrics.stop_time.value, 0)
+        self.assertTrue(metrics.motor_stopped.is_set())
+
+    async def test_detection_regions_not_suppressed_after_manual_move(self) -> None:
+        # the symptom of the bug: object detection stops entirely because motion
+        # boxes are never promoted to detection regions again
+        controller = _make_move_controller(autotracking_enabled=False)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        for later_frame_time in (1001.0, 1060.0, 4600.0):
+            with self.subTest(frame_time=later_frame_time):
+                self.assertFalse(
+                    ptz_moving_at_frame_time(
+                        later_frame_time,
+                        metrics.start_time.value,
+                        metrics.stop_time.value,
+                    )
+                )
+
+    async def test_metrics_written_when_autotracking_enabled(self) -> None:
+        # get_camera_status resets stop_time once the camera reports IDLE, so the
+        # autotracking path keeps its motion estimation timestamps
+        controller = _make_move_controller(autotracking_enabled=True)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        self.assertEqual(metrics.start_time.value, 1000.0)
+        self.assertEqual(metrics.stop_time.value, 0)
+        self.assertFalse(metrics.motor_stopped.is_set())
+        self.assertTrue(
+            ptz_moving_at_frame_time(
+                1001.0, metrics.start_time.value, metrics.stop_time.value
+            )
+        )
+
+
 if __name__ == "__main__":
     unittest.main()

frigate/test/test_review_manual_event_severity.py

@@ -0,0 +1,154 @@
+"""Tests for manual event severity categorization.
+
+Regression coverage for manual events created via the events API being
+categorized as detections when their label appears in both the alerts and
+detections label lists. Alert labels must win, matching how tracked objects
+are categorized, and labels in neither list must default to alerts so the
+historical behavior of the API is preserved.
+"""
+
+import unittest
+
+from frigate.config import FrigateConfig
+from frigate.review.maintainer import ReviewSegmentMaintainer
+from frigate.review.types import SeverityEnum
+
+BASE_CONFIG = """
+mqtt:
+  enabled: False
+cameras:
+  front_door:
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.0.1:554/video
+          roles:
+            - detect
+    detect:
+      width: 1920
+      height: 1080
+      fps: 5
+%s
+"""
+
+
+class TestManualEventSeverity(unittest.TestCase):
+    def _make_maintainer(self, review_config: str = "") -> ReviewSegmentMaintainer:
+        """Build a maintainer without invoking __init__ (avoids needing ZMQ
+        sockets, shared memory, and clip dirs). Only the config is read when
+        categorizing a manual event label."""
+        maintainer = ReviewSegmentMaintainer.__new__(ReviewSegmentMaintainer)
+        maintainer.config = FrigateConfig.parse_yaml(BASE_CONFIG % review_config)
+        return maintainer
+
+    def test_defaults_to_alert(self) -> None:
+        maintainer = self._make_maintainer()
+
+        self.assertEqual(
+            maintainer.get_manual_event_severity("front_door", "person"),
+            SeverityEnum.alert,
+        )
+
+    def test_unlisted_label_defaults_to_alert(self) -> None:
+        maintainer = self._make_maintainer(
+            """
+    review:
+      detections:
+        labels:
+          - dog
+"""
+        )
+
+        self.assertEqual(
+            maintainer.get_manual_event_severity("front_door", "pir_sensor"),
+            SeverityEnum.alert,
+        )
+
+    def test_detection_label_is_detection(self) -> None:
+        maintainer = self._make_maintainer(
+            """
+    review:
+      alerts:
+        labels:
+          - person
+      detections:
+        labels:
+          - pir_sensor
+"""
+        )
+
+        self.assertEqual(
+            maintainer.get_manual_event_severity("front_door", "pir_sensor"),
+            SeverityEnum.detection,
+        )
+
+    def test_alert_label_wins_over_detection_label(self) -> None:
+        maintainer = self._make_maintainer(
+            """
+    review:
+      alerts:
+        labels:
+          - person
+      detections:
+        labels:
+          - person
+          - dog
+"""
+        )
+
+        self.assertEqual(
+            maintainer.get_manual_event_severity("front_door", "person"),
+            SeverityEnum.alert,
+        )
+
+    def test_sub_label_is_stripped_before_categorizing(self) -> None:
+        maintainer = self._make_maintainer(
+            """
+    review:
+      alerts:
+        labels:
+          - person
+      detections:
+        labels:
+          - person
+"""
+        )
+
+        self.assertEqual(
+            maintainer.get_manual_event_severity("front_door", "person: Bob"),
+            SeverityEnum.alert,
+        )
+
+    def test_alert_label_is_detection_when_alerts_disabled(self) -> None:
+        maintainer = self._make_maintainer(
+            """
+    review:
+      alerts:
+        enabled: False
+        labels:
+          - person
+      detections:
+        labels:
+          - person
+"""
+        )
+
+        self.assertEqual(
+            maintainer.get_manual_event_severity("front_door", "person"),
+            SeverityEnum.detection,
+        )
+
+    def test_no_severity_when_alerts_disabled_and_label_not_a_detection(self) -> None:
+        maintainer = self._make_maintainer(
+            """
+    review:
+      alerts:
+        enabled: False
+      detections:
+        labels:
+          - dog
+"""
+        )
+
+        self.assertIsNone(
+            maintainer.get_manual_event_severity("front_door", "pir_sensor")
+        )